The Top 25 Python 3 Interview Questions To Prepare For In 2023

Python is one of the most popular programming languages today due to its versatility, readability and huge community support As Python continues to grow in popularity, especially with the rise of data science, machine learning and web development, so does the demand for Python developers

This means that Python coding interviews are becoming increasingly common when applying for Python developer roles. While Python is often praised for being easy to learn, Python job interviews will still test your knowledge and experience with the language.

To help you ace your next Python interview and land your dream programming job, here are the top 25 most common and important Python interview questions and answers for 2023

1. What are the key features of Python 3?

Some of the most important Python 3 features include:

• Improved syntax and readability with features like keyword-only arguments
• Advanced string formatting using f-strings
• Support for unicode text by default
• Enhanced generators and iterators
• Faster I/O performance
• Improved memory management and the concurrent.futures module
• Type hints for code clarity and tooling
• Built-in asyncio module for async/await programming

2. How is Python 3 different from Python 2?

While Python 3 retains most of the core language features. some key differences from Python 2 include

• Print is a function not a statement
• All strings are unicode by default
• New syntax for variable annotations and type hints
• Integer division returns float values
• Some changes to exception handling and raising
• Removal of older features like xrange()
• Changes to map, filter, zip which return iterators instead of lists

Overall Python 3 emphasizes clarity, consistency and safety compared to Python 2.

3. How can I write code that works in both Python 2 and 3?

To write Python code that is compatible with both Python 2 and 3, you need to:

• Import features only available in Python 3 using try/except blocks
• Use conditional imports like six to write compatible code
• Use the __future__ module to enable Python 3 features in Python 2
• Avoid features removed in Python 3 like xrange
• Handle differences in string handling explicitly
• Use io module for files instead of open() directly
• Define __str__() and __repr()__ properly for classes
• Make integer division explicit using float(x) / y
• Consider using a compatibility layer like six for cleaner dual-version code

4. What is variable type inference in Python 3?

Python 3 aims to make code clearer and more foolproof by allowing type inference for variables based on initialization instead of explicit declarations.

For example:

x = 5 # x inferred as int typex = "Hello" # x inferred as str type

Type inference helps avoid bugs by detecting mismatches while also making code more concise by reducing redundancy.

5. How do you specify variable types in Python 3?

While Python uses dynamic typing and infers types during assignment, you can provide type hints using:

• Function annotations – def func(param: int) -> str:
• Type comments – num: int = 5
• The typing module – from typing import List
• Generic types – list[int]

Type hints are not enforced but help document code and enable type checking by tools like mypy.

6. What are function annotations in Python 3?

Function annotations allow you to specify the argument types and return type of a function in Python 3 using a special syntax:

def add(x: int, y: int) -> int:    return x + y

While annotations don’t enforce types, they provide useful documentation and allow static type checking. Annotations are stored in the __annotations__ attribute of functions.

7. How do you format strings in Python 3?

Python 3 introduces a new string formatting method called formatted string literals or f-strings. These use an f before the string literal and allow referencing variables inside braces:

name = "John"print(f"Hello {name}")

F-strings provide a concise and readable way to embed expressions in strings without using % or format().

8. What are the most common built-in data structures in Python 3?

The most commonly used built-in data structures in Python 3 are:

• Lists: Ordered collections of objects with access by index – ['a', 1, True]
• Tuples: Immutable ordered sequences of objects – (1, 2, 3)
• Sets: Unordered collections of unique objects – {1, 2, 3}
• Dictionaries: Collection of key-value pairs – {'name': 'John', 'age': 25}

Python also has built-in data types like strings, integers, booleans etc.

9. What is list comprehension in Python 3?

List comprehension provides a concise way to create lists from sequences or ranges in Python 3:

nums = [x*2 for x in range(10)] print(nums)# [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

List comprehensions can contain multiple for loops and if statements. They are faster and more concise than normal for loops appending to lists.

10. What are the key differences between tuples and lists in Python 3?

• Lists are mutable while tuples are immutable
• Lists are defined with square brackets [] while tuples use parentheses ()
• Lists allow operations like append and insert while tuples don’t support mutation
• Lists consume more memory while tuples are more memory efficient
• Elements of a tuple can be accessed faster than lists
• Tuples can be used as keys in dictionaries unlike lists

11. How do you make a copy of a list in Python 3?

To make a copy of a list in Python 3, you can use slicing:

original_list = [1, 2, 3]new_list = original_list[:] 

Copying using slicing [:] creates a shallow copy. A deep copy of nested lists can be made using copy.deepcopy().

Methods like list() or original_list.copy() only create shallow copies.

12. What is a dictionary in Python 3? How is it different from lists or tuples?

A dictionary in Python 3 maps keys to values and provides a flexible, unordered collection. The differences from lists and tuples are:

• Dictionaries are unordered while lists and tuples are ordered
• Dictionaries are accessed via keys not positions
• Dictionaries are mutable unlike tuples
• Dictionaries can store different data types together
• Dictionaries have faster lookup than lists or tuples

13. How do you merge two dictionaries in Python 3?

To merge two dictionaries dict1 and dict2 in Python 3, you can use:

merged_dict = {**dict1, **dict2}

The ** operator unpacks each dictionary into the merged dictionary. You can also use the update() method:

dict1.update(dict2)

This will merge dict2 into dict1 instead of making a new merged dictionary.

14. What are generators in Python 3?

Generators are functions that return a lazy iterator instead of a list or single value at once. They yield one item at a time using yield instead of return.

def squares(n):   for i in range(n):       yield i*i

Generators are useful for memory-efficient sequences. Elements are computed on demand instead of pre-computed like lists.

15. What does this code print?

for i in range(10):    if i % 2 == 0:        print(i)    else:        continue

This prints out only even numbers from 0 to 10. The continue statement skips odd numbers.

Output:

02468

16. How do you write a recursive function in Python 3?

A recursive function is one that calls itself to compute a result. It must have a base case to avoid infinite recursion.

For example, to calculate a factorial recursively:

def factorial(n):    if n == 0:         return 1 # base case    else:        return n * factorial(n-1) # recursive call

Recursion provides an elegant way to divide problems into simpler subproblems of the same type.

17. Is Python 3 interpreted or compiled?

Python is an interpreted language, which means the source code is executed line-by-line by an interpreter instead of being compiled to machine code all at once.

However, Python code is first compiled to bytecode which is then interpreted. The compilation to bytecode takes place automatically when executing the code.

18. What is exception handling in Python 3?

Exception handling allows you to respond to anomalous