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.