re named groups & structured results

Parse text with named groups

Lesson 18 of 20 · 11 min · Module 5: Real-world data

Regular expressions can do more than test if text matches. Named groups pull out each field by name, turning a line of text into a dictionary.

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By the end, you’ll be able to…

Capture fields with named groups
Require a full match
Turn text into structured data

The idea

(?P<level>[A-Z]+) captures text into a group called level. match.groupdict() returns all named groups as a dict. Use re.fullmatch to require that the whole line matches, and compile the pattern once with re.compile.

import re
pattern = re.compile(r"(?P<user>\w+)@(?P<domain>[\w.]+)")
print(pattern.fullmatch("[email protected]").groupdict())

01Names beat numbers

match.group(3) breaks as soon as you add a group earlier in the pattern. match.group("level") and groupdict() keep working and document what each part means.

02fullmatch vs search

search finds the pattern anywhere, so "junk 2026-10-18 …" would still match. fullmatch requires the entire string to match, which is what a line parser needs.

import re
print(bool(re.search(r"\d+", "abc123")), bool(re.fullmatch(r"\d+", "abc123")))

Output

True False

03Compile once

re.compile builds the pattern once. Reusing the compiled object in a loop over thousands of lines is faster and keeps the pattern defined in one place.

Your turn

Write parse_line(line) for log lines like 2026-10-18 08:07:03 ERROR Payment service timeout. Return {"date": "2026-10-18", "time": "08:07:03", "level": "ERROR", "message": "Payment service timeout"}, or None if the line does not match. Levels are uppercase words; there may be several spaces after the level. Then write count_levels(text) that returns a dict counting each level, ignoring lines that do not match.

What the checks look for

Parses a log line into named fields
Allows several spaces after the level
Returns None for lines that do not match
Counts levels and skips malformed lines

Try it in the editor →

Take this with you

Named groups turn lines of text into dictionaries you can work with.

Go a little further

Return the messages of all ERROR lines that happened between two given times.