Processing Text with Python

Python
import re
import textwrap
from difflib import unified_diff
from string import Template

# Regex matching
match = re.search(r'(\d+)-(\d+)', "range: 10-20")
if match:
    start, end = match.groups()

# Text wrapping
print(textwrap.fill("A very long line of text that needs wrapping", width=20))

# String templating
t = Template("Hello, $name!")
print(t.substitute(name="World"))

# Diffing
diff = unified_diff("line1\nline2\n".splitlines(),
                     "line1\nline3\n".splitlines(), lineterm="")
print("\n".join(diff))

Progress:

• Step 1: Identify the text task category (matching, formatting, comparing, normalizing, constants)
• Step 2: Select the right module (see mapping below)
• Step 3: Write minimal, correct code using the module's idiomatic API
• Step 4: Handle edge cases (empty strings, Unicode, unmatched patterns)
• Step 5: Test with representative inputs

Module Selection Map

Example 1: Extract and validate structured data with re Input: Extract all email addresses from a block of text.

Python
import re
text = "Contact: alice@example.com or bob@test.org"
emails = re.findall(r'[\w.+-]+@[\w-]+\.[\w.-]+', text)

Output: ['alice@example.com', 'bob@test.org']

Example 2: Wrap CLI help text with textwrap Input: A long description string that must fit an 80-column terminal, indented as a bullet.

Python
import textwrap
desc = "This tool processes large datasets efficiently using streaming."
wrapped = textwrap.fill(desc, width=40, initial_indent="- ", subsequent_indent="  ")

Output:

- This tool processes large datasets
  efficiently using streaming.

Example 3: Fuzzy-match user input against known commands with difflib Input: User types "stauts", available commands are ["status", "start", "stop"].

Python
from difflib import get_close_matches
suggestion = get_close_matches("stauts", ["status", "start", "stop"], n=1)

Output: ['status']

Example 4: Safe templating with missing keys Input: Template with a placeholder not present in the substitution dict.

Python
from string import Template
t = Template("$greeting, $name!")
result = t.safe_substitute(name="Sam")

Output: '$greeting, Sam!' (no KeyError, unlike .substitute())

Example 5: Normalize Unicode for comparison Input: Compare "café" (composed) vs "café" (decomposed, e + combining accent).

Python
import unicodedata
a = unicodedata.normalize('NFC', "café")
b = unicodedata.normalize('NFC', "cafe\u0301")
a == b

Output: True