TOP 200 python interview questions for candidate screening in 2025

Why Python Interview Questions Matter More Than You Think

Let's be honest - you've probably sat through countless interviews where candidates rattled off memorized answers that told you absolutely nothing about their actual programming abilities. As someone who's been in your shoes, I get it. The challenge isn't finding people who know Python syntax by heart; it's identifying developers who can actually think and solve real problems. That's exactly why python interview questions need to be strategic, practical, and designed to reveal true programming competence rather than just textbook knowledge.

The Current Python Landscape: What Every Hiring Manager Should Know

Here's something that might surprise you: Python is now the backbone of major companies like Intel, IBM, NASA, Pixar, Netflix, Facebook, JP Morgan Chase, and Spotify. We're not talking about a niche programming language anymore - this is enterprise-level stuff. Recent surveys show that 68% of Python roles prioritize data structure knowledge, especially in backend and data engineering positions. A 2023 study revealed that senior roles increasingly focus on design patterns, which means your python interview questions need to evolve beyond basic syntax.

The language itself keeps evolving rapidly. Python 3.10 introduced structural pattern matching (essentially switch-case functionality), Python 3.8 brought us the Walrus Operator (:=), and Python 3.11 added Exception Groups. Most importantly, Python 3.13 introduced an experimental no-GIL build, with Python 3.14 adding documented free-threaded support. These aren't just technical trivia - they're indicators of where the language is heading and what your future hires should understand.

The Reality Check: What Industry Veterans Actually Say

Here's what really matters according to professionals who've been there: Jason Parent, who conducts numerous interviews at a company constantly hiring Django/Python engineers, found that questions requiring rote memorization tell us almost nothing about an applicant's actual development skills. Mitch Garnaat, creator of boto (a Python library for AWS), believes he can assess a programmer's true abilities through a relaxed, hour-long conversation. The key insight? M.T White, a seasoned software developer, warns that asking purely language-specific questions is "literally the worst thing you can do" - you can find language experts easily, but finding programming experts is the real challenge.

Ready to Transform Your Python Hiring Process?

Now that we've covered why traditional approaches fall short and what actually matters in today's Python ecosystem, it's time to get practical. We've compiled 401 best practice python interview questions for 2024 that focus on problem-solving abilities, real-world applications, and the kind of thinking that separates good developers from great ones. These questions are designed to help you identify candidates who don't just know Python - they understand how to use it effectively in your specific business context.

Because you're reading this article, you might be interested in the following article as well: Self Paced Video Interview: Tips for Employers.

🏆 The list of TOP 401 python interview questions in 2025

Is Python a compiled language or an interpreted language?

This is a great technical screening question to check a candidate’s understanding of how Python works under the hood. Look for answers that mention Python is primarily an interpreted language but also includes a compilation step.

What to look for in a strong answer:

• Mentions that Python source code is first compiled into bytecode (.pyc files)
• Explains that the Python Virtual Machine (PVM) interprets and runs this bytecode
• Acknowledges that Python is not compiled into native machine code the way languages like C++ are

Best practice tip: Ask follow-up questions like “What is the role of the Python interpreter?” or “Can you explain how Python handles code execution?” to gauge deeper understanding. Strong candidates should be able to explain the full pipeline from code writing to execution, even at a high level.

How can you concatenate two lists in Python?

Combining two lists in Python is straightforward and can be done in a couple of ways:

1. Using the `+` operator This creates a new list by joining the elements of both lists:

list1 = [1, 2, 3]
list2 = [4, 5]
result = list1 + list2
print(result)  # Output: [1, 2, 3, 4, 5]

2. Using the `extend()` method This modifies the original list by adding elements from another list:

list1 = [1, 2, 3]
list2 = [4, 5]
list1.extend(list2)
print(list1)  # Output: [1, 2, 3, 4, 5]

---

Comment:

This kind of question is great to assess basic Python skills. Look for candidates who clearly understand the difference between creating a new list vs. modifying an existing one in-place. A strong candidate might also mention when to use each approach—for example, using `+` when the original lists shouldn't be changed. This shows they think about the impact of their code.

What is the difference between a for loop and a while loop in Python?

When screening candidates for Python-related roles, this is a classic technical question to test their basic understanding of control flow.

A for loop in Python is typically used when the number of iterations is known or when you're looping through a sequence like a list, tuple, or dictionary:

for i in range(5):
    print(i)

A while loop is best when the number of iterations isn't known ahead of time, and you're looping based on a condition:

count = 0
while count < 5:
    print(count)
    count += 1

What to look for in candidate responses:

• They should point out that for loops are used when the iteration count is known or when working with sequences.
• They should explain that while loops depend on a condition and are used when it's unclear how many times the loop will run.
• Bonus if they mention potential infinite loop risks with `while`.

Best practice: A strong candidate not only knows the syntax but can give practical examples and understands when to choose one over the other based on the situation.

How do you floor a number in Python?

To floor a number in Python, use the `math.floor()` function. It returns the largest integer that is less than or equal to the given number. This means it rounds down to the nearest whole number.

Example:

import math

number = 4.9
floored = math.floor(number)
print(floored)  # Output: 4

This function is part of the standard `math` module, so you must import `math` before using it.

---

Comment:

Use the `math.floor()` function, which returns the largest integer less than or equal to the given number. It's the best practice when you want to make sure you're always rounding down, whether the number is positive or negative. Great for calculations where you need precision in downward rounding.

What is the difference between / and // in Python?

This question helps evaluate a candidate’s understanding of Python’s core syntax and how they handle data types in arithmetic operations. It’s especially useful for roles that involve Python scripting, automation, or data analysis.

A strong candidate should explain that:

• / is the standard division operator and always returns a floating point number, even if the result is a whole number. For example, `5 / 2` will return `2.5`.
• // is the floor division operator that returns the largest possible integer less than or equal to the result. `5 // 2` will return `2` because it cuts off the decimal.

Why it matters: This distinction is important in coding tasks that require precision or integer-only values, like indexing, loops, or any situation where floating point values might cause errors.

Best Practice Tip: Encourage candidates to also mention how these operators behave with negative numbers and different data types (like floats vs integers). That shows a deeper understanding of Python’s behavior.

Is Indentation Required in Python?

Yes, indentation is required in Python. Unlike some other programming languages that use brackets or other symbols to define blocks of code, Python uses indentation. This means the way your code is spaced directly affects how it runs.

Comment:

When interviewing candidates for Python roles, asking this question helps assess their understanding of how Python syntax works. Look for an answer that highlights:

• Indentation is not just style—it’s required by the Python interpreter.
• It defines code blocks like loops, functions, and conditionals.
• Improper indentation can lead to syntax errors or unexpected behavior.

A best-practice answer should be simple and to the point. If a candidate explains that indentation keeps Python code structured and readable and that it’s mandatory, that’s a strong indicator they know basic Python rules. If they also mention that consistent indentation is key (typically 4 spaces), even better.

Can we Pass a function as an argument in Python?

Yes, functions can be passed as parameters to other functions because they are objects. Higher-order functions are functions that can take other functions as arguments.

---

Why This Matters in Screening Candidates

This question is great for understanding a candidate’s grasp of Python’s core concepts, including first-class functions and functional programming. When a developer knows this, they’re likely comfortable with writing flexible, reusable code.

What to look for in a good answer:

• A clear yes backed by a short example.
• Mention of concepts like first-class objects, higher-order functions, or lambda functions.
• Bonus if they talk about use cases like passing callback functions or using `map`, `filter`, or `sorted` with custom function arguments.

Best practice approach: Ask them to write a small function that takes another function as a parameter. This will show both their understanding and coding style.

---

Red flags:

• They say “no” or hesitate excessively.
• They are unfamiliar with key terms or usage.
• They give overly complex examples that miss the core point.

---

Follow-up prompt: “Can you give me a quick example of a function that takes another function as an argument and uses it?” This helps evaluate coding fluency and comfort with abstraction in Python.

What is a dynamically typed language?

In a dynamically typed language, you don’t have to tell the program what type of data a variable holds—it figures it out while the program is running. That means the type is checked at runtime, not when the code is written. So, instead of manually defining that a variable is an integer or string, the language handles it automatically based on what value is assigned.

Some common examples of dynamically typed languages are:

• Python
• JavaScript
• Ruby
• PHP

If you are interested in more JavaScript interview questions, check out this article: JavaScript Interview Questions

Comment:

This question helps you gauge a candidate's understanding of how different programming languages manage variable types. Look for responses that focus on runtime type checking and mention the lack of explicit type declarations. A good answer might even mention benefits (like flexibility) and drawbacks (like potential runtime errors). This shows practical awareness—not just textbook knowledge.

Best practice: Ask follow-up questions like, "What are some pros and cons of using a dynamically typed language in a large codebase?" to dig deeper into their thinking.

What is pass in Python?

The `pass` statement in Python is a placeholder that does nothing. It's used when a line of code is required syntactically, but you don’t want to execute anything.

For example, if you’re still working on a function or class and haven’t written the logic yet, you can add `pass` to avoid errors.

def my_function():
    pass

This won't throw an error even though the function body is empty. It’s helpful in the early development stages for:

• Empty functions or methods
• Placeholder for future code
• Empty loop or conditional blocks

---

Comment:

When screening candidates for Python roles, this question helps test their basic understanding of Python syntax and development practices. A strong candidate should mention that:

• `pass` lets you write syntactically correct code without functionality
• It’s mainly used as a temporary placeholder
• It avoids syntax errors when code is incomplete

A great answer also shows they’ve encountered it in actual development. Best practice is to use `pass` only when necessary and replace it with real code as development progresses.

How are arguments passed by value or by reference in Python?

When asking this question, you're testing a candidate’s understanding of how Python works under the hood. The key here is to hear if they mention that Python uses a "pass-by-object-reference" model. That means:

• Immutable objects (like integers, strings, tuples): behave like pass by value
• Mutable objects (like lists, dictionaries, sets): behave like pass by reference

A solid candidate will explain that when a variable is passed into a function, the reference to the object is passed, not the actual object itself. However, whether changes inside a function affect the variable outside depends on if the object is mutable or not.

Best practice: Look for candidates who explain both behaviors clearly and can provide short examples. It shows they really understand the difference and can avoid common bugs. Understanding this concept deeply is vital for writing clean, predictable code in Python.

What is a lambda function?

A lambda function is an anonymous function in Python. It’s a way to write simple, one-line functions without giving them a formal name. Lambda functions can take any number of arguments but are limited to a single expression.

Example:

add = lambda x, y: x + y
print(add(5, 3))  # Output: 8

Comment:

Look for candidates who clearly explain that a lambda function is a short-hand for defining simple functions. They should mention that it doesn’t require the regular `def` keyword and is useful when a function is needed temporarily—for example, when sorting or filtering data.

Best practices to watch for in a candidate’s response:

• Understanding of lambda usage in real-world scenarios like maps or filters.
• Awareness that lambdas are limited to one expression — no multiple statements.
• Clarity that it's anonymous, meaning it doesn't require a name unless assigned.

If a candidate can explain that lambda functions are mainly used for quick tasks where defining a full function would be overkill, that's a good sign they understand Python well.

What is List Comprehension? Give an Example.

List comprehension is a concise way to create lists in Python. It allows candidates to loop through an iterable, apply a condition, and generate a new list all in a single line of code. It's considered Pythonic and shows the candidate's understanding of clean and efficient code.

Example:

squares = [x**2 for x in [2, 3, 4, 5]] 
print(squares)  # Output: [4, 9, 16, 25]

Comment:

This question is great for screening junior to mid-level developers. A strong candidate should not only explain what list comprehension is but also provide a correct example. The ideal response shows both the syntax and the output.

Best practice: Look for candidates who can also explain why they'd use list comprehension—such as for cleaner, shorter code or performance reasons. If someone confuses it with a regular for loop, it's a good moment to dig deeper into their knowledge of Python syntax and coding style.

What are args and *kwargs?

When interviewing candidates for a Python developer role, a common and effective technical question is:

"Can you explain what args and kwargs are in Python, and give an example of how you’d use each?"*

This question checks the candidate's understanding of flexible function arguments—an important concept in writing clean and adaptable code.

Comment:

• args allows a function to accept any number of positional arguments as a tuple.
• **kwargs allows a function to accept any number of keyword arguments as a dictionary.*

Best practice tip: Look for candidates who don’t just explain the syntax correctly, but also provide clear, real-world examples. A strong answer might include using args to sum any number of numbers, or using *kwargs to build flexible API functions. This question also gives insight into their ability to write reusable and scalable code.

What is a break, continue and pass in Python?

Break, continue, and pass are three control flow statements in Python used inside loops and conditionals. Understanding how they work helps evaluate a candidate’s ability to manage flow and logic efficiently in a program.

• break: Stops the loop entirely, even if it hasn’t reached the end.
• continue: Skips the current iteration and moves to the next one.
• pass: Does nothing; it’s a placeholder used to avoid syntax errors when a statement is legally required but doesn’t need to do anything yet.

---

Comment:

Look for candidates who can clearly explain these terms with simple examples. The best answers will show awareness of how and when to use each—especially break and continue, as they’re more common in real-world coding. If the candidate mentions pass being used in function or class placeholders, that’s a plus. This shows a good understanding of Python syntax structure.

What is the difference between a Set and Dictionary?

When screening candidates for a technical role, especially in software development or data-related positions, this question helps assess their understanding of core Python data structures.

Strong candidates should highlight the following points:

• A Set is an unordered collection of unique elements. It's used when you need to store elements without duplicates, and it’s defined using `{}` or the `set()` constructor.

• A Dictionary is also an unordered collection, but it consists of key-value pairs. It’s used for storing data that’s associated or mapped, and it's defined using `{key: value}` within curly braces.

Example of a good answer:

"A Set is a group of unique elements like `{1, 2, 3}`, and you can't have duplicates in it. A Dictionary stores data in a `key: value` format like `{'a': 1, 'b': 2}`. While sets are great for membership testing, dictionaries are ideal for fast lookups by key."

Best practice tip: Follow up by asking for real-world scenarios where each would be most useful. This gives insight into how well the candidate can apply concepts in practical work situations.

What are Built-in data types in Python?

Python has several built-in data types that are used to store and manipulate data. These types are the foundation of Python programming and help define the kind of value a variable can hold.

The main categories of built-in data types in Python are:

• Numeric types: `int`, `float`, `bool`, `complex`
• Sequence types: `str`, `list`, `tuple`, `range`
• Mapping type: `dict`
• Set types: `set`, `frozenset`
• Binary types: `bytes`, `bytearray`, `memoryview`
• NoneType: `None`

Comment:

Look for candidates who mention a range of types such as Numeric (int, float, bool, complex), Sequence Types (string, list, tuple, range), and Mapping Types like dictionary. Strong answers may also include Set types and binary types. A well-rounded response shows they understand how data is stored and manipulated in Python. This is a basic question for technical roles—look for confidence and clarity in the explanation, not just memorized lists.

What is the difference between a Mutable datatype and an Immutable data type?

This question helps you assess a candidate’s understanding of memory management and data handling in programming—especially important for software development roles.

Comment:

Best practice is to look for a clear and confident explanation. The candidate should highlight that:

• Mutable data types (like Lists and Dictionaries) can be changed after they are created. For example, items can be added, removed, or updated.
• Immutable data types (like Strings and Tuples) cannot be modified after creation. Any change creates a new object in memory.

A strong answer might also include examples and briefly explain the implications on performance and debugging. If the candidate struggles here, it may show a gap in foundational knowledge, making it a valuable screening question for coding roles.

What is a Variable Scope in Python?

Variable scope in Python defines where in your code a variable can be accessed or modified. It helps control the visibility and lifetime of variables, making your code cleaner and less prone to bugs.

There are four main types of variable scope in Python:

• Local Scope: This applies to variables declared within a function. They can only be used inside that function.
• Global Scope: These are variables defined outside of any function. They can be accessed anywhere in the script but must be declared as `global` inside functions to modify.
• Enclosing (or Nonlocal) Scope: These come into play with nested functions. Inner functions can access variables of their enclosing (outer) functions.
• Built-in Scope: These are names preassigned by Python like `print()` or `len()`. They are always available.

---

Comment:

When interviewing candidates, ask them to clearly explain each type of scope and when they would use them. The best practice is to see if they understand the difference between declaring and accessing a variable, especially how Python handles scope in nested functions. A solid answer should mention local vs. global and touch on nonlocal scope as part of nested functions. Good candidates might even reference the LEGB Rule (Local, Enclosing, Global, Built-in).

How is a dictionary different from a list?

When interviewing candidates for roles that involve coding or data handling, you might ask this question to test their understanding of data structures.

Best Practice: Look for clear, concise answers with real-world usage examples. This shows not only technical knowledge but also practical thinking.

Comment:

A list is an ordered collection of items, where each item is accessed by its index (a number starting from 0). Think of it like a to-do list — every item has a position.

A dictionary, on the other hand, is an unordered collection of key-value pairs. You access the values using unique keys instead of position. It's like a contact book where you look up a name to get the phone number.

• Use lists when the order matters or when you need to loop through items in order.
• Use dictionaries when you need to store related information and access it quickly using a label or key.

A great answer from a candidate would include the difference and also when to use each — that shows deeper understanding beyond just definitions.

What is docstring in Python?

A docstring in Python is a special kind of comment used to describe the purpose and behavior of a module, function, class, or method. It helps anyone reading the code understand what it does without needing to read every line.

Docstrings are written using triple quotes (`""" """` or `''' '''`) and should be placed right after the definition of the function, class, or module. Here's an example:

def greet(name):
    """This function greets the person whose name is passed as a parameter."""
    print(f"Hello, {name}!")

You can access the docstring with the `doc` attribute or by using the built-in `help()` function:

print(greet.__doc__)
help(greet)

Best practice: Always include a docstring when writing functions or classes. It improves code readability, helps other developers (and your future self), and is essential for auto-generating documentation.

---

Comment:

Python documentation strings provide a convenient way of associating documentation with Python modules, functions, classes and methods. Declared using triple quotes and accessed using `doc` method or `help` function.

How is Exceptional handling done in Python?

Exceptional handling in Python is managed using three main keywords: `try`, `except`, and `finally`. These help control what happens when an error or unexpected behavior occurs in your program.

• `try` block: Used to test a block of code for errors.
• `except` block: Executes if an error occurs in the `try` block.
• `finally` block: Runs no matter what—whether an error occurred or not.

This structure is key to writing robust and error-resilient programs. It helps prevent the program from crashing and allows developers to handle problems more gracefully.

Comment:

Using `try`, `except`, and `finally` is the correct Python syntax for error handling. The best practice is to use `try` to wrap any code that may potentially throw an error. If an error does happen, the `except` block will catch it and let the program continue running smoothly. You can also use `finally` to close files or clean up resources, no matter what happens in the `try` or `except`. When screening candidates, expect them to explain this clearly and maybe even provide a basic code example.

What is the difference between Python Arrays and Lists?

When interviewing candidates for Python-related roles, ask: "Can you explain the difference between Python Arrays and Lists?"

This is a great way to evaluate their practical understanding of Python's data structures.

Comment:

Arrays and Lists might seem similar, but they serve different purposes.

• Arrays (from the built-in `array` module) store elements of the same numeric type. They're more memory-efficient and can be faster when working with large sets of numbers.
• Lists are Python's built-in, general-purpose data structures. They're flexible, letting you mix different data types in one list. Lists also have more built-in functions and methods, making them easier to work with in many scenarios.

Best practice: For numeric data and performance-sensitive applications, arrays might be better. But for most general-purpose tasks, lists are the go-to choice in Python. This kind of question tests not just technical knowledge but also when to choose the right tool. Look for candidates who understand the trade-offs—not just the definitions.

What are Modules and Packages in Python?

Modules in Python are single files (ending in `.py`) that contain Python code. They can hold functions, classes, and variables—you can reuse them in different programs by importing them.

Packages are collections of related modules grouped in a directory. A package includes an `init.py` file, which tells Python that the directory should be treated as a package.

This structure helps keep your project organized, especially as it grows. You can use built-in modules like `math`, or create your own.

---

Comment:

This question checks whether a candidate understands Python's basic structure and how to organize code. A good developer should know how to create and import modules and packages. Best practice is to structure code in reusable modules to support clean, maintainable code. Look for clear, simple explanations and examples in the candidate's answers.

What is the difference between xrange and range functions?

This question is great for screening Python developers, especially when you want to assess their understanding of Python versions and memory efficiency.

In Python 2, both `range()` and `xrange()` are available:

• `range()` returns a full list of numbers.
• `xrange()` returns a generator-like object that produces numbers as needed (lazy evaluation).

This difference makes `xrange()` more memory-efficient, especially useful when dealing with large ranges.

In Python 3, `xrange()` was removed and `range()` now behaves like `xrange()` from Python 2. So, it doesn’t return a list, but rather a range object that generates numbers on demand.

Best Practice:

Listen for candidates who can explain the difference clearly and demonstrate awareness of Python versions. A strong answer might include use cases like optimizing loops for performance or managing memory in large-scale applications.

Red flag: If a candidate doesn't seem to know that `xrange()` no longer exists in Python 3, it could mean they aren't up to date with current Python standards.

What is Dictionary Comprehension? Give an Example

Dictionary comprehension is a quick and clean way to create dictionaries in Python using a single line of code. It’s similar to list comprehension, but builds key-value pairs instead.

Example:

{chr(65+i): i+1 for i in range(5)}

This will return:

{'A': 1, 'B': 2, 'C': 3, 'D': 4, 'E': 5}

Comment:

Look for candidates who not only define dictionary comprehension correctly but also give a clear, working example. A good answer shows they understand both the syntax and real-world use case. Best practice is to test this concept in coding roles where Python is frequently used. The ideal candidate should explain how dictionary comprehension improves code readability and efficiency.

Is Tuple Comprehension possible in Python? If yes, how and if not why?

No, tuple comprehensions are not directly supported in Python. If you try writing something like `(i for i in (1, 2, 3))`, it actually creates a generator expression, not a tuple.

Python supports list comprehensions and set/dictionary comprehensions, but tuple comprehension doesn’t exist in the same form. If you want to build a tuple using a generator expression, the right way is to pass the generator to the `tuple()` constructor — like this:

tuple(i for i in (1, 2, 3))

This is a best practice to generate tuples dynamically while still writing clean and efficient code. It's a common area of confusion during tech interviews, so it's a good screening question to test not just syntax knowledge but understanding of how Python iterables work. Look for candidates who can explain the difference clearly between generators, lists, and tuples.

Differentiate between List and Tuple?

When you're interviewing for roles that involve Python programming, asking candidates to differentiate between a list and a tuple is a great way to test their foundational understanding of data structures.

Key Differences:

• Mutability: Lists are mutable (you can change them), while tuples are immutable (you cannot change them once created).
• Memory Usage: Lists consume more memory. Tuples are lighter because they are fixed.
• Performance: Lists are slower to iterate over because of their flexibility. Tuples offer faster iteration.
• Use Cases: Lists are ideal when you need a dynamic collection. Tuples fit use cases where data should not change (like dictionary keys or fixed data sets).

Best Practice: Look for candidates who not only know these differences but can explain when and why to use one over the other in real-world scenarios. Bonus if they mention how immutability leads to better performance or safer code in concurrent applications.

What is the difference between a shallow copy and a deep copy?

When you ask a candidate about the difference between shallow and deep copies, you're tapping into their understanding of memory management and object references—a key concept in many programming languages like Python, Java, or JavaScript.

Shallow Copy:

• Copies the references to the objects.
• Changes made to nested objects in the original will reflect in the copy.
• Typically faster and uses less memory.

Deep Copy:

• Copies the actual content and structure.
• Changes in the original object do not affect the copy.
• Requires more processing time and memory.

Best Practice When Screening:

Look for candidates who clearly explain both types, ideally with a coding example. Listen for mentions of why and when they would use one over the other. This shows practical understanding beyond theory.

A high-quality answer should include:

• An understanding of object references and how changes affect the original or copy.
• Explanation of performance trade-offs.
• Mention of libraries or built-in functions like `copy.copy()` and `copy.deepcopy()` in Python, for example.

This question not only tests knowledge but also explores how the candidate writes safe, maintainable code.

Which sorting technique is used by sort() and sorted() functions of Python?

When you ask a candidate, “Which sorting technique is used by sort() and sorted() functions of Python?” — you’re testing both their technical depth and familiarity with commonly used programming tools.

Best answer: Python uses the Tim Sort algorithm, which is a hybrid sorting algorithm derived from merge sort and insertion sort. It’s designed for performance on real-world data and is known for its stability and efficiency.

Why it matters in screening:

• A technical candidate should know more than just how to use functions—they should understand what’s under the hood.
• Tim Sort is optimized for partially ordered datasets and performs in O(N log N) in the worst case, which shows Python’s attention to practical performance.
• Understanding stability in sorting (i.e., maintaining the relative order of equal elements) matters in scenarios like sorting objects with multiple fields.

Pro tip: Look for candidates who mention:

• Tim Sort
• Stability of the algorithm
• The hybrid nature (merge + insertion sort)
• Real-world performance optimization

This indicates both knowledge and the ability to apply theory in practical development work.

What are Decorators?

Decorators are a feature in Python that allow you to modify or enhance the behavior of a function or method without changing its actual code. In simple terms, a decorator is a function that takes another function as an input, adds some kind of functionality to it, and returns a new function.

This makes decorators incredibly useful for tasks like logging, access control, timing, or memoization—reusable features that you might want to apply across many functions.

Here’s a quick example:

def my_decorator(func):
    def wrapper():
        print("Something before the function runs.")
        func()
        print("Something after the function runs.")
    return wrapper

@my_decorator
def say_hello():
    print("Hello!")

say_hello()

When you run this, the `say_hello()` function will now be wrapped with the code in the decorator. It prints additional messages before and after the actual function output.

---

Best practice tip: Use @functools.wraps inside your decorator to maintain the original function’s name and docstring. This helps with debugging and code readability—very useful for clean coding standards.

When screening candidates, ask them:

• Can you explain what decorators are and why they’re useful?
• Can you write a simple decorator from scratch?
• How have you used decorators in real-world projects?

What to look for: Look for a clear understanding of how decorators work and when to use them. A strong candidate can both explain the concept and give practical examples. Bonus points if they mention the importance of using `functools.wraps` or can explain how decorators are used in frameworks like Flask or Django.

💡 Remaining 371 questions...

The online article only includes the first 30 questions to keep it digestible, but we’ve put together an ebook for you with all the questions we gathered through our extensive research.

Download E-Book here 👉

Real-World Insights: How Industry Experts Approach Python Interview Questions

The recruiting landscape for Python developers has evolved significantly, and industry veterans have some fascinating perspectives on what works and what doesn't when screening candidates.

Jason Parent, who works at a company constantly hiring Django/Python engineers, has conducted countless interviews and discovered something crucial: asking questions that require rote memorization tells us almost nothing about an applicant's skill as a developer. His experience highlights a common pitfall many recruiters fall into when designing their python interview questions.

Mitch Garnaat, the creator of boto (a popular Python library for interfacing with AWS), takes a completely different approach. He believes he can have a relaxed, hour-long conversation with someone and walk away with a really good idea of how skilled a programmer they are or could become. This conversational method focuses more on problem-solving abilities than memorized syntax.

Ilia Gilmijarow, who works as a technical interviewer at one of the largest IT companies, uses structured technical interview questions when recruiting developers. His systematic approach in a high-volume hiring environment shows how python interview questions can be standardized while still being effective.

Perhaps the most interesting perspective comes from M.T White, a software developer, author, and self-described "future Sith master." He strongly advises against asking language-specific questions, calling it "literally the worst thing you can do." His reasoning? It's easy to find an expert in any given language, but quite difficult to find an expert in programming itself.

Tony Flury, a Python software developer since 2011, offers practical advice for interviewers: if you can't answer the questions yourself, you shouldn't be conducting the interview. This simple rule ensures that python interview questions are both relevant and fair.

The impact of these approaches is real. User @rtharun1153 recently commented: "Thank you so much. I cracked my 1st interview with the help of this video!" - showing how the right preparation and question format can lead to successful matches between candidates and companies.

Why Video Screening Software Is Revolutionizing Python Developer Recruitment

The recruitment community is rapidly embracing video screening software for several compelling reasons. Traditional in-person interviews can be time-consuming and scheduling nightmares, especially when dealing with python interview questions that require technical demonstrations.

Video screening allows recruiters to:

• Assess coding skills in real-time without geographical limitations
• Record sessions for team review and collaborative decision-making
• Standardize the interview process across multiple candidates
• Reduce time-to-hire significantly

Modern video screening platforms enable candidates to showcase their problem-solving approach rather than just final answers - exactly what experts like Mitch Garnaat value in their conversational interview style. This technology bridges the gap between efficiency and effectiveness in technical recruiting.

The adoption rate has skyrocketed because video screening solves the core challenge: how do you evaluate technical skills while respecting everyone's time? With features like code sharing, real-time collaboration, and instant playback, recruiters can make more informed decisions about Python developers.

Ready to streamline your Python developer recruitment process? Discover how video screening software can transform your hiring strategy at candidatescreenings.com and start making better hiring decisions today.