Remove Numbers
Delete all digits (0–9) from your text, leaving only letters, spaces, and punctuation.
What Is Remove Numbers?
A remove numbers tool strips all digit characters (0–9) from your text, leaving only letters, spaces, punctuation, and symbols. This is useful when you have mixed text-and-number data but only need the textual content — product descriptions that include model numbers you want stripped before analysis, contact lists where phone number fields have crept into name fields, or body copy that contains statistics you need to remove before a particular processing step.
Removing numbers can also serve as a quick way to clean natural language text for keyword analysis or word frequency counting where numeric tokens would distort results. In some content workflows, placeholder numbers need to be stripped before localization or republishing. The tool processes all digit characters simultaneously, making it faster than using find-and-replace for each digit (0, 1, 2... 9) individually.
See also: Remove Special Characters, Find & Replace, Remove Extra Spaces, Trim Whitespace
Order 66 was placed on 1 Jan 2024→Order was placed on Jan Who Should Use This Tool?
Strip numeric tokens from text datasets before word frequency analysis, keyword extraction, and natural language processing pipelines.
Remove version numbers, model numbers, and statistics from template content before repurposing text across products or time periods.
Quickly strip numeric content from mixed text-number strings during data transformation and cleaning operations.
Key Use Cases
- →Remove statistics and numerical data from body copy before repurposing as a template for updated content.
- →Strip model numbers and product codes from imported product descriptions before keyword analysis.
- →Clean contact data where phone numbers or IDs have been concatenated with name or address fields incorrectly.
- →Prepare text corpora for NLP tasks by removing all numeric tokens to focus analysis on vocabulary and word patterns.
- →Remove year references and version numbers from documentation before redistribution with updated version information.
How to Use Remove Numbers
- Paste or type your text into the Input Text box.
- The result appears instantly on the right.
- Click Copy to copy the output to your clipboard.
- Click Clear to reset and process new text.
Common Mistakes & Pro Tips
- !Removing numbers from data where they carry meaning — phone numbers, years, measurements, and codes are content, not noise. Only use this tool when the numbers are genuinely unwanted for the specific use case.
- !Expecting decimal points to be removed — this tool removes digit characters (0-9) only. The decimal point "." and other number-adjacent symbols like "$" and "%" are not digits and will remain in the output.
Frequently Asked Questions
Everything you need to know about Remove Numbers
Does this remove decimal points and currency symbols?
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No — only digit characters (0–9) are removed. Periods, commas, currency signs ($, €, £), percentage signs (%), and other number-adjacent characters are not digits and remain in the output. "Price: $29.99" becomes "Price: $." after digit removal. If you also need to remove these symbols, follow with Remove Special Characters.
Will phone numbers be cleanly removed?
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Digit removal strips all digit characters, so "+1 (555) 123-4567" becomes "+ () -" — the digits are gone but the parentheses, spaces, and dashes remain. To fully remove a phone number pattern, you'd need to also strip the remaining punctuation or use a more targeted find-and-replace with a phone number pattern. For a full phone number clean, use Find and Replace with a phone pattern regex.
What happens to ordinal numbers like "1st", "2nd", "3rd"?
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The digits are removed, leaving the ordinal suffix: "1st" → "st", "2nd" → "nd", "3rd" → "rd". These remnants look strange in text. If ordinal numbers appear in your text and you want to handle them cleanly, consider replacing them with words first ("first", "second", "third") using Find and Replace before removing numbers.
How is this different from removing special characters?
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They target different character sets. Remove Numbers targets only digit characters (0-9). Remove Special Characters targets punctuation, symbols, and non-alphanumeric characters. Letters are preserved by both. The key difference: Remove Numbers keeps symbols but strips digits; Remove Special Characters keeps letters and numbers but strips symbols. You can run both sequentially if you need letter-only output.
Can I use this to clean phone number fields in a CSV?
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For simple digit removal from a text field, yes. But CSV data is better handled with a spreadsheet formula or script to target specific columns without affecting the entire file. In Excel, =SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(A1,"0",""),"1","")...) can strip digits from a cell. In Python with pandas: df["phone"].str.replace(r"\d", "", regex=True). These column-targeted approaches are safer than pasting the whole CSV through a text tool.
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Last reviewed: August 2026