Python is among the most popular and sought-after programming languages today. Major organizations in the world build programs and applications using this object-oriented language. But before you can get your hands on it, you have to go through a lot of basic interview questions. We have outlined the best 33 Python interview questions that are commonly asked at companies such as Google, Facebook, Amazon, NASA, and more!

Python is a general-purpose high-level programming language. It was designed to be readable and easy to learn, with syntax that can be described as “simple”. Python can be used for many smart tasks, from web development to machine learning. It’s most commonly used by data scientists and software developers alike.

Here are the 33 interview questions you can use to assess Python candidates’ skills to determine whether they know the language well.

Most Commonly Ask Python Interview Questions

What type of language is python? Programming or scripting?

Python is capable of scripting, but it is primarily used as a general-purpose programming language.

1. What are some of the most common Python tasks you perform on a daily basis?

This question will show you if the candidate is familiar with some of the most common tasks in Python. And, how they go about performing them.

2. What is an object-oriented programming language?

An object-oriented programming language has three basic features – data structures are objects made up of fields; operations on these objects override each other as they share common field names, and messages can be sent between different objects.

3. What can Python do?

Python’s versatility is one of its main advantages. It can be used for web programming, statistics processing, and data mining; it has libraries that have been ported to many platforms with a wide range of hardware specifications.

4. What are the pros?

Here are a few Python pros:

• Versatility: Python can be applied in various fields such as web development, statistic processing or data mining. The language also has ports to different operating systems which supports a variety of hardware configurations
• Community Support: For beginners who want help on their coding projects but don’t know where to start there is always some way online they could find tutorials and other helpful members from Python’s community that will give them advice when needed – just type “python tutorial” into Google!
• Simple and Easy: Python is equipped with a simple syntax and easy-to-read documentation.
• Open Source: The language’s source code is open for anyone to review and contribute to what they want in the future of this programming language

5. What are the cons?

Here are a few Python cons:

• Standard Library Issues: Some users have experienced some issues with certain libraries that don’t work as expected, but these problems can be easily solved by downloading more updated versions or taking other measures
• Difficult to Learn: Python is not hard, but it’s more difficult than JavaScript and Java. Learning the syntax can be challenging for beginners.
• Harder Code Too Read: The code written in Python is harder to read because of its indentation style, which may cause problems when collaborating with other developers.

6. What are some popular uses?

Python has been used by Google and YouTube since 2006 as their primary programming language for most applications – from search algorithms to ad systems!

Other notable companies that use this language include Cisco Systems, NASA Ames Research Center, Instagram (Facebook), Quora (Microsoft), Reddit (Yahoo!), Yahoo!, TwitchTV (Amazon), and Pinterest. (This means you could work at one of these companies!).

7. What is the future of Python?

Python continues to grow in popularity as it becomes more popular and accessible. It offers an effective solution for tasks such as data analysis, web development, scripting, and scientific computing. (Cool! This means that there will be plenty of jobs available in the near future if you want one.)

Python is among the most popular languages today for data scientists.

Python is a very popular language for data scientists because it’s interpreted, the syntax is simple (read: easy to learn), and there are many libraries available.

In-Dept Technical Python Interview Questions

In no particular order, here are a few in-depth technical questions you should ask the candidate

8. What are the key features of Python?

• Unlike languages such as C and its variants, Python is an interpreted language. Interpreted languages require little to no compilation before they are run. Other popular interpreted languages include PHP and Ruby.
• Python is a dynamically typed language, meaning that you don’t need to specify the type of variables when they are declared. For example, x = 111 and then x = “I’m a string” without error.
• Python is object-oriented programming language that supports classes, composition, and inheritance relationships. This means that Python does not have package protection as C++ does.
• Python programming is fast but the running can be slower than languages that do not take time to compile. Fortunately, Python allows C-based extension modules so optimization can take place and bottlenecks are often removed. An example of this kind of module is NumPy. It’s really quite quick because a lot of the number-crunching it does isn’t actually done by Python
• Python provides the key to many doors of programming and is widely considered one of the most sought-after languages. It’s also used as ‘glue’ code to help make other technologies or languages work together.

9. What is namespace in Python?

A namespace is a naming system used to organize objects. It can be used by programmers to avoid naming conflicts and help increase readability in Python programming

10. What are the advantages of using namespace?

Namespaces make it easier for a programmer to write readable code, prevent name clashes or collisions between different variables with the same names and assign unique attributes or values to objects without assigning them conflicting variable names

11. Are there any disadvantages of using namespace?

The disadvantage is that they may cause complications if a program has lots of sub-modules because these modules must each have their own namespace which means more lines of code will need to be written. Furthermore, this system makes debugging much harder as well since when an error occurs elsewhere in the program than where you’re looking at it’s hard to find what’s happening.

12. What is the difference between an iterable and a generator?

An iterable object produces values on demand, while a generator yields them one at a time via an iterator

13. What are some of the arguments against Python not being statically typed?

Some believe that it can be difficult to determine whether or not certain objects have been passed in as parameters because there’s no way to check their types ahead of time; therefore, if you misspell something like “print” for example then your program won’t compile which may lead programmers into spending more time debugging than they would otherwise spend

14. What is the difference between lists and tuples in Python?

A list in Python is an ordered collection of elements that allows duplicate entries, but tuples are a set of immutable sequences.

15. How do I create a string from another data type?

You can convert any other data type to strings using the str() function. If you use it with numbers too, it will output their values as text instead (as opposed to just showing them on-screen).

16. How do I find the index of a string?

To get the indexes, use: “string” .”[start_index:end_index]”

The first bracket indicates which character you’re looking for in a string. The second bracket specifies where to stop searching the search text. If you replace both with asterisks (e.g., *) it means that Python will iterate through every single letter in your given alphabet and return their respective positions in order from highest to lowest.

17. Explain split() and join() functions in Python?

split(): Separate a string into pieces. Join: Put the strings parts together to form one complete string.

18. How many data structures are there in Python?

There are four types of data structures: strings, tuples, lists, and dictionaries. Strings can be used as “labels” to identify a point on an object – the values for those labels would be found in other fields (e.g., integers or floats). Tuples store related items like coordinates or string indices together so they’re easily sorted through by their value names; Lists group variables according to certain criteria, while Dictionaries are collections that map keys to values like this: {“key”: “value”}.

19. What is Scope in Python?

Every object in Python functions with its own scope. Variables declared inside a function are local to that function, and not accessible outside of it – this is called the “local” or “non-global” scope. On the other hand, variables enclosed in an outer set of parentheses (e.g., ()), curly braces ({}), or brackets [] belong to the entire program rather than just one part of it; these are known as global variables because they can be accessed anywhere in your code.

20. How is memory managed in Python?

Memory management in Python is pretty simple. It’s managed by the interpreter, which determines when and where to allocate data for objects.

21. What is PYTHONPATH?

It is an environment variable which is used to define a list of directories that the Python interpreter should search when looking for modules and scripts.

22. How do you import packages in PYTHONPATH?

To import packages from within your own directory, use:

import mypackage.mymodule or import packageName as modulename (e.g., “spam”) to get access to another module located elsewhere on your computer’s filesystem – it is possible to import different versions of the same module without any conflict by assigning each version its own name

23. What are python modules?

Python modules are files containing Python code.

24. What are python packages?

Python Packages are sets of files that relate to a particular topic, usually held in one directory on the filesystem. They can be installed using pip or easy_install and we import them as any other module with:

import packageName (e.g., “spam”)

25. Is python case sensitive?

Yes. Python is case-sensitive. For example, if you have a function called “myFunc()”, then the following would result in an error:

def myfunc():

26. What is type conversion in Python?

Type conversion refers to the process of converting from one data type to another.

27. Are there any other ways to convert types?

Yes, Python provides conversion functions for all built-in types: int(), float(), complex() and str().

28. What is the difference between Python Arrays and lists?

Arrays and Lists in Python are both sequence types, which means they can be indexed and sliced. The difference is that arrays are immutable whereas lists are mutable (i.e., it’s possible to change a list item).

29. What are functions in Python?

A function is a block of code that is written to be executed by the Python interpreter.

30. What are classes in Python?

Classes are blueprints for objects, and they’re used as templates from which new instances can be created.

31. How do you run programs with arguments using “python”?

The syntax for running python scripts includes providing command line parameters at the end of your script’s filename: $ python myProgram arg0 argN

32. What is the difference between range & xrange?

xrange and range are the exact same in terms of functionality, but xrange is faster. But they both provide you a way to generate a list of integers to use.

33. What are arrays in Python?

Arrays can be seen as ordered lists of objects, with the items indexed by an index value or key.

FYI: Why Python is best for Data Science

Python is one of the most popular and sought-after programming languages by data scientists in tech. Python has long been thought of as a simple programming language to pick up on the syntax level.

What’s also great is that Python has a tight-knit community with resources for just about anything you might need, so it continues to be an optimal choice for people wanting to take advantage of emerging technologies like machine learning and data science.

Data science is the process of extrapolating useful information from large stores of data. The raw data has often been collected through various registers and other sources, but may not be correlated in any meaningful way. So, Data Scientist leverage machine learning techniques to find correlations between disparate datasets, and then use these findings to predict future trends and outcomes.

Python is a perfect language for data science because it’s quite versatile so you can employ different techniques as needed, its syntax lends well to scientific applications with the numpy library being one of Python’s more popular packages that support vectorized computations often required in machine learning and other numerical algorithms.

Machine Learning might sound like an advanced field, but all it takes are some simple concepts such as regression analysis which will find patterns among variables. One of the best ways to learn about machine learning is by playing around with OpenAI’s Universe toolkit or Kaggle competitions – they both offer easy-to-use interfaces where you get immediate feedback on your work!

Here are ways a Data Scientist uses Python:

• Data wrangling
• Machine Learning
• Text Mining
• Clustering and Visualization
• Statistical Analysis – Optimization Problems
• And more!

We hope these Python interview questions help you in your search for the best Python engineer.

And, as always, if you need help, please reach out.