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 op­ti­miza­tion.

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 minimizes 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 organize code. Having in­di­vid­ual files helps improve read­abil­i­ty and main­te­nance.
• Package: Packages are also used for or­ga­niz­ing but are struc­tured in di­rec­to­ries and folders. This allows modules in the code to be organized 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 se­ri­al­iz­ing and de­se­ri­al­iz­ing 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 se­ri­al­izes 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 de­se­ri­al­izes 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 lin­eariza­tion 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 lin­eariza­tion 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 au­tho­riza­tion 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 or­ga­niz­ing 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 pri­or­i­tizes 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.