Replace Words with Digits
Normalize datasets by mapping verbal numbers to digits. Sanitize text with case-sensitive matching and whole-word logic for secure data ingestion.
1
Word-to-digit Rules
2
Error: Unable to replace words with digits.
Please configure parameters and execute the action.
About Replace Words with Digits
Replace Words with Digits swaps words in text for digits or number strings using custom rules. You can limit the replacements to standalone words only and optionally require exact case matching.
How It Works
Use the tool in three quick steps:
- Paste text with words - Add the content that contains words to replace.
- Enter word-to-digit rules - Write one replacement rule per line in the format "word=digit".
- Generate updated text - Click Replace Words to apply the rules.
Basic Examples
-
Replace words with digits
Input Text: cat dog cat Word-to-digit Rules: cat=1 dog=2 Output: 1 2 1
-
Only replace full words
Input Text: app appetite app Word-to-digit Rules: app=4 Replace Whole Words: checked Output: 4 appetite 4
-
Match word case exactly
Input Text: zoo Zoo ZOO Word-to-digit Rules: zoo=1 Zoo=2 ZOO=3 Case Sensitive Words: checked Output: 1 2 3
Real-World Usage Scenarios
- Data Anonymization - PII Protection - Researchers often need to mask participant names or sensitive identifiers before sharing datasets. By setting rules like 'John=001' and 'Jane=002', you can quickly convert identifiable text into a numeric-based anonymized format while maintaining data structure.
- Log File Standardization - Status Mapping - System administrators can normalize log files by converting verbal status indicators into numeric severity levels. Mapping 'Success=1', 'Warning=2', and 'Critical=3' allows for easier sorting and analysis in spreadsheet software or database imports.
- Inventory and SKU Transformation - E-commerce managers use this to convert descriptive product attributes into standardized numeric category codes for warehouse management systems. For instance, mapping 'Small=10', 'Medium=20', and 'Large=30' helps generate clean CSV upload files.
- Text-to-Code Preparation - Developers can use the tool to replace variable names or constants with specific numeric IDs when preparing pseudo-code or configuration files that require integer-based inputs rather than string labels.
Frequently Asked Questions
How do I define multiple replacement rules?
Rules must be entered one per line using the 'word=digit' format. For example, to replace 'Red' with '1' and 'Blue' with '2', you would enter 'Red=1' on the first line and 'Blue=2' on the second.
Can I prevent the tool from replacing parts of longer words?
Yes. Enable the 'Replace Whole Words' option. This ensures that a rule for 'cat' will not affect the word 'category', only the standalone word 'cat'.
Does the tool distinguish between 'Error' and 'error'?
By default, the tool is case-insensitive. If you need to target specific capitalizations, check the 'Case Sensitive Words' option to treat 'Error' and 'error' as distinct strings.
Is there a limit to how many rules I can add?
There is no hard limit on the number of rules. However, for extremely large datasets or thousands of rules, performance depends on your browser's processing capabilities.
Text Tools
Other tools you might like
Map Latin characters to Unicode cursive glyphs. The logic handles Mathematical Alphanumeric exceptions to ensure cross-platform compatibility and parsing.
Parse string architecture into vector graphics. Map tokens, whitespace, and punctuation to distinct hex layers. Export precise SVG schematics for analysis.
Parse and sanitize string buffers by mapping hard breaks to custom separators. Employs paragraph-aware logic to maintain semantic data integrity.
Parse corrupted strings to strip non-spacing marks. Normalize Unicode input by removing recursive combining characters. Restore data integrity now.
Parse and normalize character sequences via Unicode point values. Sanitize strings using skip lists, case logic, and duplicate removal for clean datasets.
Shift characters cyclically across strings. Map offsets to reformat multiline structures with line-by-line logic. Normalize text for data schemas.
Shift printable ASCII characters by 47 positions to obfuscate sensitive strings. Implement symmetric mapping for range 33-126 to ensure data integrity.
Parse and shift alphabetic characters 13 positions. Maintain case sensitivity and non-letter integrity for spoiler protection or data obfuscation.
Sanitize datasets with custom mapping and whole-word logic. Apply recursive double-pass processing to clean whitespace. Normalize your data structure.
Map specific vowel patterns using custom substitution logic. Supports case-sensitive matching and secondary passes to sanitize or obfuscate string data.
Normalize datasets by converting tabs, newlines, and spaces into custom symbols. Collapse whitespace clusters to ensure strict character counts.
Normalize strings using custom character rules. Execute case-sensitive matching and recursive replacement passes to ensure data integrity. Export clean results.
Map consonants to custom characters using iterative substitution rules. Sanitize strings with case-sensitive precision for technical datasets and linguistics.
Sanitize raw data by mapping CRLF sequences to custom delimiters. Collapse repeated breaks and trim whitespace to ensure valid dataset parsing.
Map numeric sequences to cardinal words. Parse standalone digits or specific patterns. Optimized for TTS data prep and document sanitization logic.
Parse and reformat datasets by mapping commas to custom symbols. Logic-aware processing preserves numeric separators while collapsing redundant clusters.
Parse raw strings to eliminate specific character sets. This utility handles case-sensitive matching and collapses redundant whitespace for clean datasets.
Sanitize stylized Unicode glyphs into standard Latin script. Parse decorative fonts for screen reader accessibility and database safety [UTF-8].
Strip leading and trailing quotation marks from individual words. Recursive logic handles nested delimiters in SQL, JSON, and CSV datasets efficiently.
Sanitize datasets by stripping outer quotation marks. This tool parses multi-layer quotes and trims whitespace to ensure clean SQL or CSV formatting.