Coding Patterns Quick Reference: Your Complete Guide

15 essential coding patterns (sliding window, two pointers, BFS/DFS, dynamic programming) with Python and JavaScript solutions

What This Guide Covers

This interactive reader gives you a comprehensive overview of Coding Patterns Quick Reference. In the full product you will find:

• Core Concepts: Deep explanations of every topic covered
• Practical Examples: Real-world code and configuration samples
• Best Practices: Patterns and approaches used by experienced professionals
• Quick Reference: Cheatsheets and lookup tables for daily use

Who This Is For

Software engineers preparing for coding interviews. Competitive programmers and anyone wanting to master algorithmic patterns.

How to Use This Reader

The first two chapters of this guide are available free. Use the table of contents on the left to navigate. The remaining chapters are available in the full product.

Ready for the complete guide? Scroll to the paywall section at the bottom of chapter 2 to unlock everything.

Chapter Overview

Sliding Window & Two Pointers

Master the sliding window pattern for substring, subarray, and sequence problems. Two-pointer techniques for sorted arrays and linked lists. Python and JavaScript.

What You Will Learn

This chapter provides an in-depth look at sliding window & two pointers. You will understand the core concepts, see practical examples, and learn best practices used by experienced professionals.

Core Concepts

Let us start with the fundamentals. Every topic in this guide builds on a solid foundation of core concepts that you need to understand before moving to advanced material.

Key Principles

1. Understand the problem first — Before applying any solution, make sure you understand what you are trying to solve

2. Compare alternatives — Every approach has trade-offs. Know what you are giving up

3. Measure before optimizing — Data beats intuition. Always measure before making changes

Practical Example

# Real-world coding pattern implementation function

def sliding_window_max(nums: list, k: int) -> list:
    """Find maximum element in each sliding window of size k."""
    from collections import deque
    dq, result = deque(), []
    for i, n in enumerate(nums):
        while dq and dq[-1] < n: dq.pop()
        dq.append(n)
        if i >= k - 1:
            result.append(dq[0])
            if dq[0] == nums[i - k + 1]: dq.popleft()
    return result

Best Practices

When working with the concepts in this chapter, keep these best practices in mind:

• Start simple — Build the simplest version first, then iterate
• Document decisions — Record why you chose one approach over another
• Test your assumptions — Verify your understanding with small experiments
• Review with peers — Code review catches issues before they become problems

Summary

In this chapter, we covered the essential concepts of sliding window & two pointers. These form the foundation for the more advanced topics in the following chapters.