🧠 A Practical Guide to CS Fundamentals, Python Tricks & Interview-Ready Patterns
In the world of software engineering, fundamentals are not optional—they’re foundational. Whether you’re preparing for a technical interview or just brushing up on core concepts, revisiting the essentials can be a career-defining move. This post is a curated walkthrough of CS fundamentals, Pythonic tools, and best practices—designed to be both a refresher and a launchpad.
📌 The Interview Mindset:
Before diving into syntax or data structures, let’s talk about approach. In interviews, it’s not just about solving the problem—it’s about how you solve it.
✅ Coding Best Practices to Keep in Mind:
- Clarify the problem: Don’t assume. Ask about edge cases, constraints, and input types.
- Think out loud: Your thought process is as important as your final answer.
- Start with brute force: Then optimize. Show your evolution.
- Write readable code: Use meaningful variable names and modular functions.
- Test manually: Walk through your code with sample inputs.
- Explain trade-offs: Time vs. space, readability vs. performance.
🧱 Design Patterns:
Design patterns are reusable solutions to common software design problems. They’re not code templates—they’re mental models.
Common Patterns to Know:
- Singleton: Ensures a class has only one instance.
- Factory: Creates objects without exposing instantiation logic.
- Observer: One-to-many dependency between objects.
- Strategy: Encapsulates interchangeable behaviors.
- Decorator: Adds behavior to objects dynamically.
Knowing when and why to use these patterns can elevate your design discussions in interviews.
🐍 Python Helper Functions:
Python’s standard library is a goldmine for solving problems efficiently. Here’s a categorized list of built-in functions and idioms that often come in handy:
🔤 String Utilities
s.isdigit(), s.isalpha(), s.lower(), s.strip(), s[::-1], s.split(), s.count()📋 List Operations
sorted(), sum(), min(), max(), list.append(), list.pop(), list.count()📚 Dictionary & Set Operations
dict.get(), dict.items(), set.add(), set.intersection(), set.difference()🔢 Math & Conversion
abs(), round(), pow(), bin(), hex(), int('101', 2), float('3.14')🔁 Functional Tools
map(), filter(), zip(), enumerate(), any(), all()🔣 ASCII & Unicode
ord('A') # 65
chr(65) # 'A'🧰 Data Structures in Python: Stack, Queue, Heap
🥞 Stack (LIFO)
stack = []
stack.append(x)
stack.pop()Or use collections.deque for better performance:
from collections import deque
stack = deque()
stack.append(x)
stack.pop()📬 Queue (FIFO)
from collections import deque
queue = deque()
queue.append(x)
queue.popleft()Or use queue.Queue for thread-safe operations.
⏫ Priority Queue (Min-Heap)
import heapq
heap = []
heapq.heappush(heap, x)
heapq.heappop(heap)To simulate a max-heap:
heapq.heappush(heap, -x)
-max(heapq.heappop(heap))🔁 deque vs heapq: Know the Difference
| Feature | deque |
heapq |
|---|---|---|
| Type | Double-ended queue | Binary heap (min-heap) |
| Insert/Remove | O(1) at both ends | O(log n) |
| Use Case | Queues, stacks | Priority queues, heaps |
🧮 Time Complexity Reference Table
Here’s a quick cheat sheet for the time complexity of key operations in each structure:
| Operation | list |
deque |
heapq (min-heap) |
set / dict |
|---|---|---|---|---|
| Append (end) | O(1) amortized | O(1) | O(log n) | — |
| Pop (end) | O(1) | O(1) | O(log n) | — |
| Pop (start) | O(n) | O(1) (popleft) |
— | — |
| Insert (start) | O(n) | O(1) (appendleft) |
— | — |
| Search | O(n) | O(n) | O(n) | O(1) |
| Membership Test | O(n) | O(n) | O(n) | O(1) |
| Push to Heap | — | — | O(log n) | — |
| Pop from Heap | — | — | O(log n) | — |
| Peek Min (heap[0]) | — | — | O(1) | — |
| Get by Key | — | — | — | O(1) (dict) |
🧠 Final Thoughts
Mastery isn’t about memorizing APIs—it’s about understanding patterns, recognizing trade-offs, and writing code that communicates intent. Whether you’re preparing for interviews or just refining your craft, these tools and techniques will serve you well.
If you found this useful, I’d love to hear what you’re brushing up on—or what you’d like to see next. You can also check out my other technical deep dives here.
Happy coding. 🚀