Regular Expressions (the re module)

A regular expression is a tiny pattern language for describing text — "any digit", "an email", "a word at the end of a line". Python's re module turns those patterns into powerful search-and-replace tools.

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Once you can read and write regex, validating phone numbers, pulling dates out of log files, or cleaning messy data becomes a few lines instead of a hundred.

A regular expression (regex) is a string that describes a pattern of text. Instead of asking "does this string contain the exact word cat?", regex lets you ask "does this string contain three digits, then a dash, then four digits?".

Everything starts with importing the module — it ships with Python, nothing to install:

That pattern \d{' '}-\d{' '}-\d{' '} reads as: "three digits, a dash, three digits, a dash, four digits". You just described a US phone number in 17 characters.

Regex patterns are full of backslashes ( \d , \w , \b ). But in a normal Python string, \ is the escape character — "\n" means newline, "\t" means tab. That clash causes endless confusion.

The re module has many functions, but 95% of real work uses just five:

Character classes — shortcuts for common groups

By default quantifiers are greedy : they grab as much as possible. Add a ? to make them lazy — grab as little as possible. This trips up everyone the first time:

Parentheses (...) create a capture group . After a match you can pull out each group individually — perfect for parsing structured text.

Named groups ( (?P<name>...) ) make patterns self-documenting and let you reference fields by name instead of counting parentheses.

Here's the kind of validation code that runs behind every registration form. Notice how each rule is a small, readable pattern:

The "perfect" email regex is famously gigantic. In real apps, a simple pattern like the one above plus sending a confirmation email is the practical standard. Don't try to validate every RFC edge case with regex alone.

If you reuse a pattern, compile it once into a Pattern object . It's faster and reads better in loops:

Common flags: re.IGNORECASE (case-insensitive), re.MULTILINE ( ^ / $ match each line), re.DOTALL ( . also matches newlines).

These lines extract all hashtags from a tweet and print them lowercased — but they're scrambled. Put them in the correct order:

Import first, define the data, run findall to collect the captures, then loop. The capture group (\w+) means findall returns just the word, without the # .

Read each snippet and predict what prints before revealing the answer.

['1', '22', '333'] — \d+ grabs each run of one-or-more digits as a separate match.

hello_big_world — \s+ matches each run of whitespace (even multiple spaces) and replaces the whole run with a single underscore.

False — re.match only checks the START of the string, and "the cat" starts with "the". Use re.search to find it anywhere.

You can now describe text patterns like a pro!

You've learned the five core re functions, character classes, quantifiers, greedy vs lazy matching, and capture groups. Regex is a skill you sharpen by using it — keep a cheat sheet handy and test patterns on small strings first.

🚀 Up next: Dates & Times — work with the datetime module to parse, format, and do arithmetic with dates.

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