8 Must‑Know Python Features Every Job Seeker Should Master

Job hunting season is approaching, and solid Python fundamentals are crucial. Below are eight representative Python‑feature questions with detailed explanations to aid interview preparation.

01. Iterator and Generator

Question example: Change the brackets in a list comprehension from [] to () – does the data structure change?

Explanation: An iterator follows the iteration protocol and can be obtained via iter() . A generator uses yield to produce values on demand, automatically implementing __iter__ and __next__ . Generators are more concise and memory‑efficient; they can replace list comprehensions. The answer is yes – the structure becomes a generator, as shown in the code image.

02. Decorator

Question example: Write a decorator that checks the current year.

Explanation: A decorator is a callable that adds extra functionality to a function without modifying its code. It is useful for logging, performance testing, transactions, caching, etc. The provided code demonstrates wrapping a function to enforce a year check.

Without the decorator, test("backbp") would return Hello backbp, 2020 Happy . The decorator changes the output because the current year is 2019.

03. Monkey Patch

Question example: What is a monkey patch in Python?

Explanation: Monkey patching dynamically modifies classes or modules at runtime, allowing third‑party code to be patched without altering the original source. The example shows assigning MyClass.func = ClassName.monkey_func and then calling obj.func() , which changes the output to monkey_func .

<code>MyClass.func = ClassName.monkey_func
obj.func()</code>

04. == vs is

Question example: Explain the difference between == and is in Python.

Explanation: is checks object identity (equivalent to id(x) == id(y) ), while == checks value equality by invoking the object's __eq__() method. The accompanying image shows that two variables with the same value may not be identical.

05. Shallow Copy vs Deep Copy

Question example: Explain shallow and deep copying in Python.

Explanation: copy.copy() creates a shallow copy – the top‑level object is duplicated, but nested objects share the same memory. copy.deepcopy() recursively copies all nested objects, producing completely independent structures. Images illustrate the behavior.

06. Mutable vs Immutable Types

Question example: Describe the difference between mutable and immutable types in Python, with examples.

Explanation: Mutable types (e.g., list , dict ) store a reference; modifications affect the same memory address. Immutable types (e.g., str , int , tuple ) create a new object on change. Images demonstrate the behavior.

07. Python Introspection

Question example: Briefly explain Python's introspection features.

Explanation: Introspection allows a program to discover object types and attributes at runtime using functions such as type() , dir() , getattr() , hasattr() , and isinstance() . The image shows examples of these functions.

08. Underscore Conventions

Question example: Explain the three underscore forms _foo , __foo , and __foo__ in Python.

Explanation: _foo denotes a “private” name, omitted from from module import * . __foo triggers name mangling to avoid clashes in subclasses (e.g., becomes _ClassName__foo ). __foo__ denotes magic methods or attributes like __init__ , __name__ , etc.

Single leading underscore: private convention.

Double leading underscore: name mangling for class attributes.

Double leading and trailing underscores: special (magic) methods.

These eight Python language‑feature questions are essential knowledge for any candidate preparing for autumn recruitment. Feel free to leave comments if you have questions.