What are the 10 most common Python interview questions? (And how to answer them)

Python is a popular programming language. So if you’re applying for developer jobs, you should count on getting detailed questions about how it works. Here we look at 10 Python interview questions you might get and how to answer them.

Python interview question 1: What is special about Python and what advantages does the language have?

Python is a versatile programming language that can be used in the fields of web development, data analysis and artificial intelligence. It offers incredible user friendliness, versatility and good performance—just three reasons why many aspiring programmers choose it as a programming language to learn.

It’s a very beginner friendly language with an easy-to-understand syntax and a comprehensive standard library that includes many ready-made modules and functions.

Other advantages include the large, active community of Python developers, who contribute external resources and support to the development process. As an interpretive and object-based language, it’s also well-suited to quickly writing code and testing it immediately. Dynamic typing further enhances the language’s flexibility.

Like Java, Python is platform independent. It can be seamlessly integrated with other languages, like C++, facilitating cross-platform work and potential performance optimisation.

Python interview question 2: What is meant by ‘scope’ in Python?

‘Scope’ refers to the area where a variable is valid. It’s the part of the code where the variable is visible and accessible, and where variables can be defined and used. It makes code clearer and minimises naming conflicts. Python has two main types of scope:

• Global scope: This type of variable is defined outside of functions and classes. It’s accessible throughout the whole program and is often found at the beginning of the code or at a higher level.
• Local scope: Variables in local scope are restricted to the function they are defined in. They can also be assigned to a specifically defined block.

If you want to access a variable outside the defined code, you’ll need to extend the scope with a special statement like ‘global’ or ‘nonlocal’.

Python interview question 3: What is the difference between lists and tuples in Python?

In Python, there are two data types for storing ordered collections of elements: lists and tuples. Lists are typically used more often due to their flexibility. Here are some crucial differences between the two:

• Mutability: Lists can be modified even after they’ve been created. You can add, remove or change elements in them. On the other hand, you cannot change the elements of a tuple after it’s been created.
• Syntax: Lists are created using square brackets [], whereas tuples use round brackets (). Unlike lists, tuples can also be defined using commas instead of brackets.
• Speed: Which data type is faster depends on the context. Lists’ mutability makes them faster in some operations, like extensive data modifications. Tuples are faster when it comes to accessing elements within a collection.

Python interview question 4: What is the difference between modules and packages?

Modules and packages differ in their applications. Modules are individual files with code, whereas packages are collections of modules within a directory. Both are meant to help create a clear structure, which can be helpful in larger Python projects. Some other differences between modules and packages are:

• Module: In Python, modules are individual files that can contain functions, classes and variables. The files have the ending .py and help to better organise code. Having individual files helps improve readability and maintenance.
• Package: Packages are also used for organising but are structured in directories and folders. This allows modules in the code to be organised hierarchically. For a directory to be treated like a package, it should contain the file __init__.py.

Python interview question 5: What are pickling and unpickling?

‘Pickling’ and ‘unpickling’ refer to serialising and deserialising internal objects. The processes make it possible to convert objects into binary data representations (pickling) or retrieve objects from binary representations (unpickling).

• Pickling: Pickling converts an object into binary representation. This is important if you want to save data permanently or transport data to a network. The pickle module is used for pickling in Python. It serialises the object by converting it into a byte stream.
• Unpickling: In a reversal of the pickling process, unpickling restores a previously pickled object from its binary representation. The pickle module is also used for unpickling and deserialises the byte stream back into a Python object.

Python interview question 6: What is the difference between a function and a lambda function?

Overall, the two function types serve the same purpose. Lambda functions are shorter and are used more frequently for simpler operations and filter tasks. The main differences between a normal function and a lambda variation are related to syntax, scope and areas of use.

• Syntax: Lambda functions have a more compact syntax when it comes to definition, body and return value. For example, there is no explicit ‘return’ for the return value, since the expression’s value is returned implicitly. That makes lambda expressions particularly well suited to short, concise function descriptions.
• Scope: While normal functions can take several statements and complex logic, lambda functions are limited to one expression. Lambda variants can only use local variables, which are typically restricted in their scope. Normal functions, on the other hand, can use both local and global variables.
• Areas of use: Normal functions can be defined anywhere in the code. Lambda variables are often used where a short-lived function like sorted, filter, or map is required.

Python interview question 7: What types of inheritance are there in Python and how does Python handle multiple inheritance?

There are several types of inheritance in Python. Both single inheritance and multiple inheritance are possible. In single inheritance, one class inherits from a single parent class and the derived class adopts all the attributes and methods of the parent class.

In multiple inheritance, the class inherits from more than one parent class. The derived class can adopt the attributes and methods of all the parent classes.

In Python, the C3 linearisation algorithm or the Method Resolution Order is used for multiple inheritance. The algorithm determines the order in which methods are resolved in a multiple inheritance hierarchy. That ensures that the attributes and methods are searched in a consistent and predictable order. Python uses linearisation to prevent known inheritance problems like the diamond problem.

Python interview question 8: What is monkey patching?

‘Monkey patching’ refers to the process of modifying existing code during runtime. For example, this can be done by adding or replacing functions or methods. Monkey patching allows dynamic changes to the code without modifying the source code of the original class or function. It can be useful for fixing errors, extending functionalities and adapting parts of libraries or frameworks. When it comes to classes, methods can also be overwritten, and new methods can be added.

Python interview question 9: What are the differences between Django, Pyramid and Flask?

Django, Pyramid and Flask are Python web frameworks that differ with respect to their approaches, complexity and available functions. Here are some of the main differences between them.

Django

Django is a high-level web framework that offers a variety of additional features. Many functions and modules are pre-installed. For example, Django has its own object-relational mapping for database interaction. It also provides an integrated admin interface that simplifies the management of data models.

The URL design and structure of the application are pre-defined, which makes development easier. Django tends to place a lot of emphasis on conventions. It also offers built-in authentication and authorisation and contains functions like formulas and CSRF protection. The framework is best suited to advanced users, as the large range of features and strict structure make for a steep learning curve.

Pyramid

In contrast to the comprehensiveness of Django, Pyramid is lightweight and flexible. It enables developers to select preferred libraries and components and is designed to be scalable and expandable. The framework supports various kinds of applications, from small projects to large, complex applications.

Unlike Django, Pyramid doesn’t have a prescribed application structure, which allows for more freedom in organising code. The choice of template engine is also open, since Pyramid doesn’t use a default one.

Its flexible application and minimal presets make its learning curve significantly flatter, making Pyramid better suited to beginners.

Flask

Flask is what’s called a microframework. It was originally designed to be lightweight and simple to use. To facilitate this, the framework only offers the essentials. If needed, libraries can be added with Flask.

Flask uses a simple and clear API that makes it possible to quickly start developing. The framework is based on the WSGI toolkit ‘Werkzeug’ and uses the Jinja2 template engine. Developers can also integrate other components as needed.

In the end, your choice of framework will depend on the needs of your project and how much flexibility is required. Django offers numerous integrated functions and clear structure. Pyramid prioritises flexibility and scalability. And Flask focuses on simplicity and minimalism.

Python interview question 10: What do ‘args’ and ‘kwargs’ stand for in Python?

The two terms stand for positional arguments (args) and keyword arguments (kwargs). Both are conventions that are often used when defining functions with a varying number of arguments, giving developers additional flexibility. This is especially useful if it’s not clear from the start how many or what kind of arguments will be provided in the end.

Args are used when a variable number of arguments will be accepted in a function based on position. This allows a non-predefined number of arguments to be entered, which in turn are available as tuples in the function.

Kwargs are similar. They’re used to accept a variable number of arguments based on keywords. This allows a non-predefined number of arguments to be entered, which are available in the function as a dictionary.

If a function needs to contain both variable positional and keyword arguments, it’s possible to use args and kwargs in the same function in Python.