What is a regular expression (regex)?

A regular expression is a pattern that describes a set of strings. Think of it as a search query on steroids - instead of searching for exact text, you search for patterns. For example, you can find all email addresses in a document without knowing what they are beforehand.

Why should I learn regex?

Regex saves hours of manual work. You can validate user input (emails, phone numbers), search through logs, clean up data, and do find-and-replace across thousands of files. Every programming language supports regex.

What does the dot (.) mean in regex?

The dot matches any single character except newline. So 'c.t' matches 'cat', 'cut', 'c9t', or 'c@t' - any three-character string starting with 'c' and ending with 't'.

What is the difference between * and + in regex?

Both are quantifiers. The asterisk (*) matches zero or more of the previous character - so 'a*' matches '', 'a', 'aa', 'aaa'. The plus (+) matches one or more - so 'a+' requires at least one 'a'. It won't match an empty string.

How do I match the start or end of a line?

Use ^ for the start and $ for the end. The pattern ^Hello matches 'Hello world' but not 'Say Hello'. The pattern world$ matches 'Hello world' but not 'world peace'.

What are capture groups in regex?

Capture groups are created with parentheses (). They save the matched text so you can reference it later. For example, in the pattern (hello)-(world), the first group captures 'hello' and the second captures 'world'. Use groups to extract specific parts of a match.

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Regular Expressions Explained

Pattern matching made simple

What is a Regular Expression?

A regular expression (regex) is a pattern you use to find text. Instead of searching for exact words, you describe what you're looking for. Want to find all email addresses in a document? All phone numbers? All dates? Regex can do that.

Think of it like this: if ctrl+F is a flashlight, regex is a metal detector. It finds things based on their shape, not their exact value.

How Regex Matching Works


flowchart LR
    A[Your Pattern] --> B[Regex Engine]
    C[Input Text] --> B
    B --> D{Match?}
    D -->|Yes| E[Found matches]
    D -->|No| F[No matches]

Try It Yourself

Test these patterns in our interactive regex tool.

Open Regex Tester

The Basics

Most characters in regex match themselves. The pattern cat matches the word "cat". Simple. But regex has special characters that do more interesting things.

. Any character (except newline)

* Zero or more of previous

+ One or more of previous

? Zero or one (optional)

^ Start of line

$ End of line

\d Any digit (0-9)

\w Any word character

Common Patterns

Email Address

Pattern: \w+@\w+\.\w+

✓ john@example.com
✓ test@company.org
✗ not-an-email

This matches: one or more word characters, then @, then more word characters, a dot, and more word characters.

Phone Number

Pattern: \d{3}-\d{3}-\d{4}

✓ 555-123-4567
✓ 800-555-1234
✗ 12345

The {3} means "exactly 3 times". So this matches 3 digits, a dash, 3 digits, a dash, then 4 digits.

Date (YYYY-MM-DD)

Pattern: \d{4}-\d{2}-\d{2}

✓ 2024-01-15
✓ 1999-12-31
✗ 01-15-2024

Four digits, dash, two digits, dash, two digits. The ISO date format.

Character Classes


flowchart TB
    subgraph "Character Classes"
        A["[abc]"] --> A1["Matches a, b, or c"]
        B["[a-z]"] --> B1["Any lowercase letter"]
        C["[0-9]"] --> C1["Any digit"]
        D["[^abc]"] --> D1["NOT a, b, or c"]
    end

Capture Groups

Parentheses () create capture groups. They save what they match so you can use it later. This is super useful for extracting parts of a pattern.

Pattern: (\w+)@(\w+)\.(\w+)

Input: john@example.com

Group 1: john
Group 2: example
Group 3: com

Now you can grab just the username, just the domain, or just the extension separately.

Flags

Flags change how the regex engine works. They go after the pattern.

g Global - find all matches, not just the first

i Case insensitive - "Cat" matches "cat"

m Multiline - ^ and $ work on each line

s Dot matches newlines too

When to Use Regex

Validating input - Check if emails, phone numbers, or URLs are in the right format.
Search and replace - Find patterns in text and replace them with something else.
Extracting data - Pull specific information out of logs, documents, or web pages.
Cleaning data - Remove unwanted characters or normalize formats.