9 Common Python Interview Questions and Answers

As a programmer, you may have encountered technical interview questions; this article summarizes nine typical Python interview questions for reference.

1: Difference between Python list, tuple, and dict

Lists are mutable dynamic arrays with O(1) random access and O(n) insertion/deletion; tuples are immutable arrays; dictionaries are mutable hash tables using open addressing for collision resolution.

2: Reverse a string or number

String reversal can be done with slicing:

<code>str1 = "长风几万里，吹度玉门关"
print(str1[::-1])</code>

Numbers can be reversed similarly:

<code># Positive integer
number = 10002
new_number = int(str(number)[::-1])
# Negative integer
number = -10002
new_number = int('-{0}'.format(str(abs(number))[::-1]))</code>

3: Python memory management

Python uses a garbage collector with reference counting; macros Py_INCREF(op) and Py_DECREF(op) adjust the reference count, and when it reaches zero, the object's __del__ destructor frees the memory.

4: Session, Cookie, Token

Session stores data on the server; Cookie is a client‑side implementation that often holds the session ID; Token (commonly used in REST APIs) carries user identity information and is sent in request headers.

5: Difference and purpose of GET and POST

GET and POST have no intrinsic protocol limits, but servers and browsers impose length constraints; GET is idempotent and safe for data retrieval, while POST is used for data submission and is not idempotent.

Idempotence means multiple identical requests produce the same effect and result.

6: Python decorators

Decorators are functions that wrap other functions to add behavior without modifying the original code. Example:

<code>def log(func):
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        result = func(*args, **kwargs)
        print(f"Finished {func.__name__}")
        return result
    return wrapper</code>

Decorators are useful for logging, performance testing, transaction handling, permission checks, and caching.

7: GIL, processes, threads, coroutines

The Global Interpreter Lock ensures only one thread executes Python bytecode at a time; threads are suitable for I/O‑bound tasks, while CPU‑bound work benefits from multiprocessing. Coroutines (async/await) provide lightweight concurrency without thread switching overhead.

8: List, dict, and set comprehensions

<code># List comprehension
list1 = [i for i in range(5)]
# Dict comprehension
dict1 = {k: v for k, v in some_dict.items()}
# Set comprehension
set1 = {x*2 for x in [1, 2]}
</code>

These comprehensions create new collections concisely and improve code readability.

9: Generators

<code># Generator expression
x = (i for i in range(10))
print(x)  # <generator object <genexpr> at 0x...>
</code>

Generators produce items lazily, saving memory for large sequences and enabling iteration without materializing the entire collection.

In summary, mastering these topics equips candidates to answer Python interview questions effectively.