| KEY | VALUE | ACTIONS |
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Find and identify text patterns in your data
\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}
\+\d{1,3}[-.\s]?\(?\d{1,4}\)?[-.\s]?\d{1,4}[-.\s]?\d{1,9}
\d{5}(-\d{4})?
[A-Za-z]\d[A-Za-z]\s?\d[A-Za-z]\d
[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}
\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}
\d{3}-\d{2}-\d{4}
(0?[1-9]|1[0-2])/(0?[1-9]|[12]\d|3[01])/\d{4}
\d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12]\d|3[01])
https?://[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}[/\w.-]*
(www\.)?[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}
\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}
[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}
[a-zA-Z0-9_]{3,20}
#[a-zA-Z0-9_]+
g = global (all matches), i = case-insensitiveTransform and sanitize your data with pattern replacement
Hide sensitive information like SSN, credit cards
\d{3}-\d{2}-\d{4}
XXX-XX-XXXX
Standardize phone numbers, dates, addresses
(\d{3})[-.](\d{3})[-.](\d{4})
($1) $2-$3
Remove unwanted characters or whitespace
\s+
(single space)
Update domains, protocols, or query params
http://([a-zA-Z0-9.-]+)
https://$1
Wrap values with tags, quotes, or identifiers
^(.+)$
ID-$1
Show only last 4 digits of card numbers
\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?(\d{4})
****-****-****-$1
() in your pattern to capture parts$1, $2, etc.Pull specific values from complex text using capture groups
Get just the domain from email addresses
[a-zA-Z0-9._%+-]+@([a-zA-Z0-9.-]+\.[a-zA-Z]{2,})
1 (first capture group)
Get area code from phone numbers
\(?(\d{3})\)?[-.\s]?\d{3}[-.\s]?\d{4}
1
Get numeric value from currency strings
\$(\d{1,3}(?:,\d{3})*(?:\.\d{2})?)
1
Pull just the year from various date formats
\d{1,2}/\d{1,2}/(\d{4})
1
Get numeric IDs from prefixed strings
[A-Z]+-(\d+)
1
Get the path portion from URLs
https?://[^/]+(/.*)
1
0 = entire match (default)1 = first () in pattern2 = second () in pattern, and so on...
col_0.toUpperCase()).
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Edit and test your transformation
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Build filter to include/exclude rows
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