Da Regex Pandas — Free Data Analytics Tutorial
Learn Da Regex Pandas in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Da Regex Pandas in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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Da Regex Pandas in Data Analytics
Regular expressions (regex) are powerful patterns for matching text. In Pandas, use str.contains(), str.extract(), str.replace() to find and clean text data.
Common patterns: \d (digit), \w (word char), . (any char), * (0 or more), + (1 or more), [] (character class), | (or).
Use cases: Extract phone numbers, validate emails, remove special characters, split names, parse structured text.
Performance: Regex is slower than simple string operations but more powerful.
Da Regex Pandas — Syntax
# Pandas regex operations: # df['col'].str.contains(r'\\d+') # has digits? # df['col'].str.extract(r'(\\d+)') # extract digits # df['col'].str.replace(r'[^\\w]', '') # remove non-word chars # df['col'].str.match(r'^[A-Z]') # starts with capital
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