Python Regular Expressions

# Python Regular Expressions

Welcome to Chapter 23! Regular expressions (regex) are powerful tools for pattern matching in strings — validating emails, extracting data, searching and replacing text.

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1. Learning Objectives #

• Understand regex patterns and metacharacters.

• Use re module functions: match, search, findall, sub.

• Write patterns for validation and extraction.

• Apply regex in real-world scenarios.

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2. Getting Started with re #

```python id="py23_ex1" import re

# search — find first match anywhere in string result = re.search(r"Python", "I love Python programming") if result: print(f"Found: '{result.group()}' at position {result.start()}")

# match — match at the BEGINNING of string result = re.match(r"Hello", "Hello World") print(result.group()) # Hello

# findall — find ALL matches numbers = re.findall(r"\d+", "I have 3 cats and 5 dogs") print(numbers) # ['3', '5']

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## 3. Regex Metacharacters

| Pattern | Meaning | Example |
|---------|---------|---------|
| `.` | Any character (except newline) | `a.c` → abc, aXc |
| `\d` | Any digit [0-9] | `\d+` → 123 |
| `\D` | Non-digit | `\D+` → hello |
| `\w` | Word char [a-zA-Z0-9_] | `\w+` → hello_123 |
| `\W` | Non-word char | `\W` → @, # |
| `\s` | Whitespace | `\s+` → spaces, tabs |
| `^` | Start of string | `^Hello` |
| `$` | End of string | `world$` |
| `*` | 0 or more | `ab*c` → ac, abc, abbc |
| `+` | 1 or more | `ab+c` → abc, abbc |
| `?` | 0 or 1 | `colou?r` → color, colour |
| `{n}` | Exactly n | `\d{3}` → 123 |
| `{n,m}` | n to m times | `\d{2,4}` → 12, 123, 1234 |
| `[]` | Character set | `[aeiou]` → vowels |
| `\|` | OR | `cat\|dog` |
| `()` | Group | `(ab)+` → ab, abab |

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## 4. Common Patterns

python id="py23ex2" import re

# Email validation emailpattern = r'^[\w\.-]+@[\w\.-]+\.\w{2,}$' print(re.match(emailpattern, "user@example.com")) # Match! print(re.match(emailpattern, "invalid-email")) # None

# Phone number phonepattern = r'\d{3}[-.\s]?\d{3}[-.\s]?\d{4}' phones = re.findall(phonepattern, "Call 123-456-7890 or 987.654.3210") print(phones) # ['123-456-7890', '987.654.3210']

# URL extraction urlpattern = r'https?://[\w\.-]+(?:/[\w\.-]*)*' text = "Visit https://python.org and http://example.com/page" urls = re.findall(urlpattern, text) print(urls)

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## 5. Search, Replace, Split

python id="py23_ex3" # sub — search and replace text = "My phone is 123-456-7890" censored = re.sub(r'\d', '*', text) print(censored) # My phone is *-*-****

# split — split by pattern data = "apple;banana,cherry orange" items = re.split(r'[;,\s]+', data) print(items) # ['apple', 'banana', 'cherry', 'orange']

# Groups — extract parts of a match pattern = r'(\w+)@(\w+)\.(\w+)' match = re.search(pattern, "Contact: alice@gmail.com") if match: print(f"User: {match.group(1)}") # alice print(f"Domain: {match.group(2)}") # gmail print(f"TLD: {match.group(3)}") # com

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## 6. Flags

python id="py23_ex4" # Case-insensitive result = re.findall(r'python', 'Python PYTHON python', re.IGNORECASE) print(result) # ['Python', 'PYTHON', 'python']

# Multiline text = "First line\nSecond line\nThird line" starts = re.findall(r'^\w+', text, re.MULTILINE) print(starts) # ['First', 'Second', 'Third']

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## 7. Practical: Data Validation

python id="py23ex5" def validateemail(email): pattern = r'^[\w\.-]+@[\w\.-]+\.\w{2,}$' return bool(re.match(pattern, email))

def validatephone(phone): pattern = r'^\+?\d{1,3}[-.\s]?\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}$' return bool(re.match(pattern, phone))

def validatepassword(pwd): checks = { "8+ chars": len(pwd) >= 8, "Uppercase": bool(re.search(r'[A-Z]', pwd)), "Lowercase": bool(re.search(r'[a-z]', pwd)), "Digit": bool(re.search(r'\d', pwd)), "Special": bool(re.search(r'[!@#$%^&*]', pwd)) } return all(checks.values()), checks

print(validateemail("test@example.com")) # True print(validatephone("123-456-7890")) # True

valid, details = validate_password("Hello123!") print(f"Valid: {valid}, Details: {details}") ``

Q1: \d matches: A) Letters B) Digits C) Whitespace D) Any char Answer: B

Q2: re.findall() returns: A) First match B) Boolean C) List of all matches D) Match object Answer: C

Q3: ^ matches: A) End of string B) Start of string C) Any position D) New line Answer: B

Q4: re.sub() does: A) Find B) Search and replace C) Split D) Match Answer: B

Q5: + quantifier means: A) 0 or more B) 1 or more C) 0 or 1 D) Exactly 1 Answer: B

Q6: [a-zA-Z] matches: A) Only lowercase B) Only uppercase C) Any letter D) Alphanumeric Answer: C

Q8: re.IGNORECASE does: A) Ignores errors B) Case-insensitive matching C) Ignores whitespace D) Ignores newlines Answer: B

Q9: () in regex creates: A) Comment B) Capture group C) Class D) Range Answer: B

Q10: . matches: A) Only dots B) Any char except newline C) Digits D) Letters Answer: B

1. 2. match() vs search()? match() checks at the beginning; search() finds first occurrence anywhere.

1. 3. Greedy vs lazy matching? * and + are greedy (match max). Add ? for lazy (match min): .*?.

1. 4. How to compile regex? pattern = re.compile(r'\d+') — improves performance when pattern is reused.

1. 5. Named groups? (?P<name>pattern) — access with match.group('name').

• re module provides regex support in Python.

• Key functions: search(), match(), findall(), sub(), split().

• Common patterns: \d (digit), \w (word), \s (space), . (any), ^/$ (anchors).

• Use raw strings (r"...") for regex patterns.

• Use flags like re.IGNORECASE and re.MULTILINE`.

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11. Next Chapter Recommendation #

In Chapter 24: Working with JSON and APIs, you'll learn to parse JSON and make API requests! 🚀