R - Regex (Regular Expressions) in R
Regular expressions are the Swiss Army knife of text processing. Whether you’re cleaning survey responses, parsing log files, or extracting features from unstructured text, regex skills will save you…
Read more →Regex
Python - Regex Match, Search, FindAll
• match() checks patterns only at the string’s beginning, search() finds the first occurrence anywhere, and findall() returns all non-overlapping matches as a list
Python - Regex Replace (re.sub)
The re.sub() function replaces all occurrences of a pattern in a string. The syntax is re.sub(pattern, replacement, string, count=0, flags=0).
Python - Regex Groups and Capturing
• Regex groups enable extracting specific parts of matched patterns through parentheses, with numbered groups accessible via group() or groups() methods
Perl Regular Expressions: Still the Gold Standard
Perl’s regex engine remains the most powerful text processing tool available. Here are patterns worth knowing.
Read more →Pandas - str.extract() with Regex
The str.extract() method applies a regular expression pattern to each string in a Series and extracts matched groups into new columns. The critical requirement: your regex must contain at least one…
Pandas - str.findall() with Regex
• str.findall() returns all non-overlapping matches of a regex pattern as lists within a Series, making it ideal for extracting multiple occurrences from text data