If you’re looking to build a career in data science, you already know how important Python will be. The significance of Python data science interview questions at interviews has risen exponentially. After all, it is the most widely used language in data science.
When preparing for a data science Python interview, you’ll need to cover all of the major Python concepts so that you’re fully prepared to answer any Python data science interview questions that come your way.
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To help you kickstart your Python interview prep, we’ve compiled a perfect list of questions for you. In this article, we’ll be covering:
- Python Data Science Interview Questions and Answers
- Sample Python Data Science Interview Questions for Practice
- FAQs on Python Data Science Interview Questions
Python Data Science Interview Questions and Answers
As you prepare for the Python data science interview questions, keep the following in mind and prepare accordingly. According to our observations, these questions have helped software engineers nail their tech interviews:
Q1. What is Python, and what is it used for?
An interpreted high-level, general-purpose programming language, Python is often used in building websites and software applications. Apart from this, it is also useful in automating tasks and conducting data analysis. While the programming language can create an array of programs, it hasn’t been designed keeping in mind a specific problem(s).
Q2. List the important features of Python.
Some significant features of Python are:
- It supports structured and functional programmings
- It developed high-level dynamic data types
- It can be compiled to byte-code for creating larger applications
- It uses automated garbage collection
- It can be used along with Java, COBRA, C, C++, ActiveX, and COM
Q3. What are the different built-in data types in Python?
Python uses many built-in data types. Some of these are:
- Number (int, float, and complex)
- String (str)
- Tuple (tuple)
- Range (range)
- List (list)
Q4. Explain how Python data analysis libraries are used and list some common ones.
The collection of data analysis libraries used in Python includes a host of functions, tools, and methods that manage and analyze data. Some of the most popular Python data analysis libraries are:
Q5. What is a negative index used for in Python?
Negative indexes in Python are used to assess and index lists and arrays from the end, counting backward. For instance, n-1 shows the last time in a list while n-2 shows the second to last.
To understand such technical concepts better, go over our Learn section. We have covered several topics in great detail to help you prepare Python data science interview questions.
Q6. What does it mean when we say that Python is an object-oriented language?
When we say Python is an object-oriented language, we mean that it can enclose codes within the objects. When the property permits the storage of the data and the method in a single unit, it is known as the object.
Q7. Explain a Python module. What makes it different from libraries?
A single file or many files containing functions, definitions, and variables created to perform certain tasks is called a module. It’s a .py extension file that can be imported at any given point and needs to be imported just once.
A library is a collection of reusable functionality of code that’ll allow users to carry out a number of tasks without having to write the code. A Python library doesn’t have any specific use but refers to a collection of modules.
This question is one of the most popular Python data science interview questions.
Q8. What is PEP8?
Coding convection PEP8 contains coding guidelines. These are a set of recommendations put together for the Python language that make the language more readable and easy to use for users.
Q9. Name some mutable and immutable objects.
The ability of a data structure to change the portion of the data structure without needing to recreate it is called mutability. These objects include lists, sets, values in a dictionary.
Immutability is the state of the data structure that can’t be tampered with after its creation. These objects are integers, strings, and keys of a dictionary.
Q10. What are generators and decorators?
The generator function is responsible for simplifying the process of creating an iterator. A decorator manipulates pre-existing functions or their output, which it does by adding, deleting, or altering characteristics.
Q11. Differentiate between %, /, and //?
% (Modulus operator) is responsible for returning a remainder after the division.
/ (Operator) it returns the quotient post division.
// (Floor division) it rounds off the quotient to the bottom.
Q12. What is the lambda function?
- These are anonymous or nameless functions.
- Lambda functions are anonymous as they aren’t declared in the standard manner using the def keyword. Further, it doesn’t even need the return keyword. Both are implicit in the function.
- These functions have their local namespace and don’t have any access to variables other than those in their perimeter list and those in the global namespace.
- Examples: x = lambda i,j: i+j
Yet another important question in this list of Python data science interview questions. So prepare accordingly.
Q13. Explain the map, reduce and filter functions.
Q14. What is the difference between range, xrange, and arange?
- range() - Returns a Python list object (a sequence of integers). It’s a BASE Python function.
- xrange() - Returns a range object.
- arange() - It’s a function in the Numpy library and can also return fractional values.
Q15. How do you differentiate between global and local variables?
Variables that are defined and declared outside a function and need to be used inside a function are called global variables. When a variable is declared inside the function’s body, it is called a local variable.
Sample Python Data Science Interview Questions for Practice
As you dig deeper and prepare for Python data science interview questions, do practice the following questions as well:
- Differentiate between lists and tuples in Python.
- What are positive and negative indices?
- Define Pass statement in Python.
- What are the limitations of Python?
- Give an example of runtime errors in Python.
- What is meant by compound data types and data structures?
- Explain with an example what list and dictionary comprehension are.
- Define tuple unpacking. Why is it important?
- Differentiate between is and ‘==’
- How do you differentiate between indexing and slicing?
- Explain zip() and enumerate() function.
- What is a default value?
- What’s the role of namespaces in Python?
- What is Regex? List some of the important Regex functions in Python.
- Differentiate between pass, continue and break.
If you want further insights into what a Python data science interview looks like and how to prepare for it, check out Understanding Technical Interviews at FAANG and How to Crack Them.
FAQs on Python Data Science Interview Questions
Q1. How do I prepare for Python data science interview questions?
While there is no fixed way to prepare for Python data science interview questions, having a good grasp of the basics can never go wrong. Some important topics you should keep in mind for Python interview questions for data science are: basic control flow for loops, while loops, if-else-elif statements, different data types and data structures of Python, Pandas and its various functions, and how to use list comprehension and dictionary comprehension.
Q2. Will Python be allowed in coding interviews?
While the simple answer is yes, it can vary from company to company. Python can be allowed in coding rounds, and several companies even use platforms such as HackerRank to conduct Python data science interview questions.
Q3. Explain Arrays in Python data science interview questions.
Arrays are a data structure, just like lists. With a number of objects of different data types, Python arrays can be repeated and have several built-in functions to handle them. Such conceptual questions play a vital role in Python data science interview questions. So keep this in mind when preparing.
Q4. Which resources to use to prepare for Python data science interview questions?
Some free resources to prepare for Python data science interview questions are CodeAcademy, FreeCodeCamp, DataCamp, Udacity, and Geeks for Geeks.
Q5. How long does it take to learn Python?
Typically, it takes around two to six months to learn the fundamentals of Python. But while you can understand the language — the basics at least — in a few minutes, it can take months or even years to master the programming language completely. However, preparing for Python data science interview questions doesn’t take too long.
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