Regular Expression

Python Regular Expression Cheat Sheet

Basics

• .: Matches any character except a newline.
• ^: Anchors the regex at the start of the string.
• $: Anchors the regex at the end of the string.
• []: Matches any one of the enclosed characters.
• |: Acts like a logical OR.

Character Classes

• \d: Matches any digit (0-9).
• \D: Matches any non-digit.
• \w: Matches any alphanumeric character (word character).
• \W: Matches any non-alphanumeric character.
• \s: Matches any whitespace character.
• \S: Matches any non-whitespace character.

Quantifiers

• *: Matches 0 or more occurrences.
• +: Matches 1 or more occurrences.
• ?: Matches 0 or 1 occurrence.
• {n}: Matches exactly n occurrences.
• {n,}: Matches n or more occurrences.
• {n,m}: Matches between n and m occurrences.

Anchors

• \b: Matches a word boundary.
• \B: Matches a non-word boundary.
• ^: Matches the start of a string.
• $: Matches the end of a string.

Groups and Capturing

• (): Groups patterns together.
• (?:...): Non-capturing group.

Character Escapes

• \\: Escapes a special character.
• \n, \t, etc.: Newline, tab, etc.

Examples

• \d{3}-\d{2}-\d{4}: Matches a Social Security Number.
• ^\w+@\w+\.\w+$: Matches a basic email address.

Flags

• re.IGNORECASE or re.I: Case-insensitive matching.
• re.MULTILINE or re.M: ^ and $ match the start/end of each line.

Methods

• re.search(pattern, string): Searches for the first occurrence of the pattern.
• re.match(pattern, string): Matches the pattern only at the beginning of the string.
• re.fullmatch(pattern, string): Matches the entire string against the pattern.
• re.findall(pattern, string): Returns a list of all occurrences of the pattern.
• re.finditer(pattern, string): Returns an iterator of match objects for all occurrences.
• re.sub(pattern, replacement, string): Replaces occurrences of the pattern with the replacement.

import re

# Example 1: Check if a string contains a number
result = bool(re.search(r'\d', 'Hello123World'))
print(result)  # Output: True

# Example 2: Extract all words from a string
words = re.findall(r'\b\w+\b', 'This is a sample sentence.')
print(words)  # Output: ['This', 'is', 'a', 'sample', 'sentence']

# Example 3: Replace all vowels with '*'
new_string = re.sub(r'[aeiou]', '*', 'Hello World')
print(new_string)  # Output: H*ll* W*rld

# Example 4: Extract domain from an email address
domain = re.search(r'@\w+\.\w+', 'user@example.com').group()
print(domain)  # Output: @example.com

Split a paragraph into sentences

input = input()
split = input.split('.')
for i in split:
    print(f"{(i).strip()}.")

Perplexity Calculation

Perplexity is a measure of how well a probability distribution or probability model predicts a sample. It is often used in the context of language modeling.

The formula for perplexity in the case of a unigram model is:

\[\text{Perplexity} = 2^{H(p)}\] \[H(p) = - \sum\_{x \in \mathcal{X}} p(x) \log_2 p(x)\]

where $ H(p) $ is the cross-entropy of the unigram model.