Python Full-Stack Interview Questions 1-5 (core Python features, Python 2 vs 3 differences, memory management, built-in data types, and mutability)
1. What are Python's key features?
Python is widely appreciated for being clear, expressive, and flexible. It helps developers focus on solving problems rather than struggling with syntax. Whether you're building APIs, server-side logic, data pipelines, or integrating with frontend applications, Python provides a clean and consistent development experience.
- Readable, beginner-friendly syntax: The language emphasizes clarity, making code easier to write and maintain.
- Rich standard library: Python ships with ready-to-use modules for networking, I/O, JSON handling, date/time formatting, testing, and more.
- Cross-platform support: Python can run on Linux, macOS, and Windows without major changes to code.
- Strong community and ecosystem: Popular frameworks like Django, Flask, and FastAPI simplify backend development; tools like pandas and NumPy support data workflows.
- Interoperability: Python works smoothly with databases, external APIs, and other languages like JavaScript on the frontend.
# Example: clean and readable logic
numbers = [1, 2, 3, 4]
squared = [n*n for n in numbers]
print(squared) # [1, 4, 9, 16]2. Difference between Python 2 and Python 3
Python 3 is the modern and actively supported version of the language. Python 2 has reached end-of-life, meaning it no longer receives updates or security fixes. In professional settings and interviews, always assume Python 3 unless stated otherwise.
- Print syntax: Python 3 uses
print()as a function rather than a statement. - Division behavior:
/performs floating-point division;//performs floor division. - Unified text model: Strings are Unicode by default, which simplifies handling of international text.
- Better standard library consistency: Many outdated behaviors were redesigned for clarity and reliability.
# Python 3 examples
print("Hello", "World", sep=", ")
print(5 / 2) # 2.5 (true division)
print(5 // 2) # 2 (floor division)3. Explain Python memory management
Python automatically manages memory through a combination of reference counting and garbage collection. This means most of the time you don't need to manually free memory, but understanding the model helps when optimizing large applications.
- Reference Counting: Each object tracks how many variables reference it. When the count reaches zero, the memory is freed.
- Garbage Collector: A separate system detects and cleans up reference cycles that reference counting can't resolve alone.
- Memory Pools: Python internally organizes memory to reduce fragmentation and improve allocation performance.
import gc
# Force a full garbage collection cycle (not usually needed)
gc.collect()4. What are Python data types?
Python provides a variety of built-in data types used to store and organize data. Choosing the appropriate type can make your code more efficient and easier to reason about.
- Numbers:
int,float,complex - Strings: Unicode text stored in
str - Lists: Ordered, mutable collections
- Tuples: Ordered, immutable collections
- Dictionaries: Key-value mappings
- Sets: Unordered unique item collections
- Booleans:
TrueorFalse
user = {"name": "Aisha", "role": "Admin"}
print(user["name"]) # Aisha5. Difference between mutable and immutable objects
Mutability describes whether an object's value can change after it is created. Understanding this helps prevent unintended side effects and bugs.
- Mutable Objects: Can change in place. Examples:
list,dict,set. - Immutable Objects: Cannot change in place; operations return new objects. Examples:
str,tuple,int.
# Mutable
items = [1, 2, 3]
items.append(4)
# Immutable
name = "John"
name = name + " Doe" # new string created