As a Python developer preparing for an interview, you’ll likely face a broad spectrum of questions that assess your expertise and skills. This article offers you a comprehensive guide to the most frequently asked Python Developer Interview Questions. You’ll also find examples of how to answer these questions, which will help you articulate your proficiency and knowledge in Python.
| Job Description | A Python Developer is responsible for writing server-side web application logic. They develop back-end components, connect the application with the other web services, and support the front-end developers by integrating their work with the Python application. |
| Skills | Proficiency in Python, Experience with Python frameworks such as Django, Flask, Knowledge of Object-Relational Mapping (ORM), Understanding of front-end technologies such as HTML5, CSS3, JavaScript, Familiarity with server-side templating languages, Understanding of accessibility and security compliance |
| Industry | Technology, Software Development, Web Development, Data Science, Artificial Intelligence, Machine Learning |
| Experience Level | Mid-level to Senior level |
| Education Requirements | Bachelor’s degree in Computer Science, Engineering or a related field is required. However, substantial work experience can sometimes be substituted for educational requirements. |
| Work Environment | Python developers typically work in an office setting. They may work individually or as part of a team of developers. Agile methodologies are commonly used. |
| Salary Range | $70,000 – $120,000 per year |
| Career Path | Python developers can move into more senior software development roles, lead developer roles, or into related fields such as data science or machine learning. They may also move into project or team management roles. |
| Popular Companies | Google, Facebook, Netflix, Amazon, Microsoft |
Python Interview Questions
What is the difference between a list and a tuple in Python?
How to Answer:
In your answer, ensure to include the key distinctions between a list and a tuple. Explain that the primary difference is that lists are mutable, meaning they can be changed after they are created, while tuples are immutable, meaning they cannot be changed after they are created. Also, mention that lists are denoted by square brackets, while tuples are denoted by parentheses.
Example:
In Python, lists and tuples are both sequence types that can store a collection of items. The key difference between the two is that lists are mutable while tuples are immutable. This means that once a tuple is created, you cannot change its contents while you can with a list. Another difference is the way they are defined. Lists are defined by enclosing the elements in square brackets [], while tuples are defined by enclosing the elements in parenthesis ().
How would you swap two variables in Python without using a temporary variable?
How to Answer:
The interviewer is looking for your knowledge of Python syntax and your problem-solving skills. You should explain how Python allows for multiple variable assignments in a single statement, which can be used to swap the values of two variables without a temporary variable.
Example:
In Python, swapping two variables can be done in a single line of code without the need of a temporary variable. Here’s how: `a, b = b, a`. In this statement, `b, a` is a tuple, and Python automatically unpacks this tuple to the variables on the left, effectively swapping the variables.
What is a decorator in Python and give an example of its use?
How to Answer:
To answer this question, first explain the concept of a decorator in Python, and then provide an example to illustrate your explanation. Be sure to convey that you understand decorators are a significant Python feature that enables more readable and expressive code.
Example:
Decorators in Python are essentially functions that add functionality to an existing function without modifying its structure. They are a very powerful tool, allowing programmers to augment the behavior of a function or method. This is done by wrapping the function you want to modify with a decorator, which can execute code before and after the wrapped function runs, thus modifying its behavior.
Consider the following example:
“`python
def my_decorator(func):
def wrapper():
print(‘Something is happening before the function is called.’)
func()
print(‘Something is happening after the function is called.’)
return wrapper
@my_decorator
def say_hello():
print(‘Hello!’)
say_hello()
“`
In this example, `@my_decorator` is used to wrap the `say_hello` function. As a result, the order of execution would be: print the first statement in `wrapper`, then `say_hello`, and finally print the second statement in `wrapper`.
Describe how Python’s garbage collection works and how you can control it.
How to Answer:
Explain the basic mechanism of Python’s garbage collection, including reference counting and the cyclic garbage collector. Also, mention the gc module and how it can be used to interact with the garbage collector.
Example:
Python’s garbage collection mechanism is two-fold. The primary mechanism is reference counting. Each object keeps count of the number of references to it. When an object’s reference count drops to zero, it is deallocated. However, reference counting alone can’t handle reference cycles – for example, an object that refers to itself. To handle these cases, Python also has a cyclic garbage collector, which can detect and collect groups of objects involved in a reference cycle. You can control the garbage collector using the gc module. For example, you can disable the cyclic garbage collector if you’re sure your code doesn’t create reference cycles. You can also manually initiate garbage collection using gc.collect().
How would you use Python’s built-in ‘enumerate’ function in a for loop?
How to Answer:
You should explain that the ‘enumerate’ function adds a counter to an iterable and returns it as an enumerate object. Then, describe how this can be used in a for loop by providing an example.
Example:
Python’s ‘enumerate’ function can be very useful in loops. When you use ‘enumerate’ in a for loop, it provides a counter for each iteration, which can be used as an index. For example:
for i, value in enumerate(my_list):
print(f’Index: {i}, Value: {value}’)
In this example, ‘i’ is the index provided by ‘enumerate’, and ‘value’ is the value at that index in ‘my_list’.
What is list comprehension in Python and when would you use it?
How to Answer:
Explain what list comprehension is in Python, mention the benefits of using it and give an example to demonstrate your understanding.
Example:
List comprehension is a syntactic construct in Python for creating a list based on existing lists. It provides a concise way to create lists where each element is the result of some operations applied to each member of another sequence or iterable. It’s useful when you need to create lists quickly and in a readable way. For example, if we want to create a list of squares for numbers from 0 to 9, we can use list comprehension like this: squares = [x**2 for x in range(10)].
What is the Global Interpreter Lock (GIL) in Python?
How to Answer:
First, briefly define what GIL is, then describe why it exists and what its impacts are on multi-threading in Python. It’s also a good idea to mention how to deal with the GIL, when multi-threading is necessary.
Example:
The Global Interpreter Lock, or GIL, is a mechanism used in CPython interpreter that allows only one native thread to execute at a time. This lock is necessary because CPython’s memory management is not thread-safe. The GIL is controversial because it prevents multi-core concurrency in a single process, which means a multi-threaded application may be slower on a multi-core processor than on a single-core processor due to the overhead of context switching. To deal with the GIL, we can use multi-processing instead of multi-threading, or use other Python interpreters like Jython or IronPython, which don’t have a GIL.
Explain what is the use of the zip() function in Python.
How to Answer:
Start by defining what the zip() function is. Then, explain its purpose and how it works with an example. Finish your answer by explaining a situation where it can be useful.
Example:
The zip() function in Python is a built-in function that allows you to combine corresponding elements from multiple iterable objects. It takes in iterables as arguments and returns an iterator of tuples, where the i-th tuple contains the i-th element from each of the argument sequences or iterables. For example, if I have two lists, one with names [‘John’, ‘Amy’, ‘George’] and one with ages [23, 25, 30], I can use the zip function to pair these two lists together like so: zip(names, ages). This would result in an iterator that generates the tuples (‘John’, 23), (‘Amy’, 25), and (‘George’, 30). The zip function can be particularly useful in situations where you have to map values of similar index in multiple iterable objects.
Explain the difference between ‘is’ and ‘==’ in Python.
How to Answer:
You should start by explaining that ‘==’ is used for equality testing, while ‘is’ checks for object identity. Then, provide examples that illustrate this difference. Finally, discuss situations where one might be preferred over the other.
Example:
‘==’ is an equality operator, and it checks whether the values of two operands are equal. If the values are equal, the condition becomes true. For example, if we have two lists a = [1, 2, 3] and b = [1, 2, 3], then a == b will return True because the values are the same. On the other hand, ‘is’ is an identity operator, and it checks whether two variables point to the same memory location. In the previous example, a is b will return False because although their values are the same, they are stored separately in memory.
What are Python Generators and how do they differ from regular functions?
How to Answer:
Start by explaining what a generator is in Python, then move on to how it differs from a regular function. A generator is a special type of function that returns an iterator object which we can iterate over (one value at a time). It is defined like a normal function, but whenever it needs to generate a value, it does so with the yield keyword rather than return.
Example:
In Python, a generator is a type of iterable, like lists or tuples. They do not store all the values in memory, instead, they generate the values on the fly. A function is a piece of code that performs a specific task and is reusable. The main difference between a generator and a regular function is that while a function returns a value and exits, a generator can pause the execution and continue from where it left off, allowing it to generate complex sequences of results over time, instead of computing them all at once and returning them in a big list for example.