Regular Expressions in Pandas — Free Data Analytics Tutorial

TL;DR: Learn Regular Expressions in Pandas in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.

Regular Expressions in 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.

# 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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