Python Data Science Interview Questions and Answers 2022 (Latest): How to Crack, Tips, FAQ, & More

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Python Data Science Interview Questions and Answers 2022 | Python Interview Questions and Answers: Dear Competitors!!! Do you need these frequently asked interview questions with answers from Python Data Science Interview Process? We think these Python Data Science interview preparation tips, which we have updated on this webpage, will help you prepare for your interview to get the best results. As the competition is very high, only a solid preparation strategy can improve candidates’ chances of getting selected. So, we will discuss some interview preparation tips to make your preparation easier. Get the latest Interview Questions Here

  • The interview helps to explain the real qualifications and qualities of the candidate in front of the interview team.
  • An interview round is the last hurdle to your dream job, so read this full article on Python Data Science Interview Questions to get an idea of ​​the types of questions to be asked in the interview.
  • This is not a one-day or monthly study that will give you success.

Python Data Science Interview

  • Keep reading for the goal you set & evaluate your performance and if stand up.
  • Are you starting to panic about the upcoming Python Data Science interview? Every candidate is afraid to face this final stage of the recruitment process which will test his knowledge and skills.
  • If you are prepared in advance for the questions for the interview, you can get full marks in the interview round.

In this article, we will discuss the many possible questions asked in the interview round. The interview was average and the questions were related to the educational background of the applicants and the projects taken during their college. Get the Upcoming & Current Private Jobs Here

How to prepare for Python data science interview questions?

While there is no standard way to prepare for Python data science interview questions, a good understanding of the basics never goes amiss. Some important topics to keep in mind for Python interview questions for data science: Basic control flow for loops, while loops, if-else-if statements, different data types and data structures in Python, pandas and their various functions, and how to use list comprehension and dictionary. Get the Graduate Jobs here

Will Python be allowed in coding interviews?

While the simple answer is yes, it varies from company to company. Python may be allowed in coding rounds, and many companies use sites to conduct Python data science interview questions. Like this more Engineering Jobs, kindly use this link to view it

How long does it take to learn Python?

Generally, it takes two to six months to learn the basics of Python. But while you can understand the language at least the basics in minutes, it can take months or even years to fully master a programming language. However, it doesn’t take much time to prepare for Python data science interview questions. To avail of exclusive job offers, join our Telegram Channel page now

Basic and Advanced Data Science Interview Questions

  • What are the differences between supervised and unsupervised learning?
  • How is logistic regression done?
  • Explain the steps in making a decision tree.
  • How do you build a random forest model?
  • How can you avoid overfitting your model?

We hope our article is informative. To stay ahead of the ever-increasing competition, you are strongly encouraged to download the previous year’s papers and start practicing. By solving these papers you will increase your speed and accuracy. For more information check website for exam patterns, syllabi, Results, cut-off marks, answer keys, best books, and more to help you crack your exam preparation. You can also take advantage of amazing Job offers to improve your preparation volume by joining in Telegram Channel page!!!

Frequently Asked Python Data Science Interview Questions 

  • 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.
  • 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 ‘==’

Python Data Science Interview Questions and Answers 

(Q) Explain how the filter function works

ANSWER: Filter literally does what the name says. It filters elements in a sequence.

Each element is passed to a function which is returned in the outputted sequence if the function returns True and discarded if the function returns


def add_three(x):
    if x % 2 == 0:
        return True        
        return Falseli = [1,2,3,4,5,6,7,8][i for i in filter(add_three, li)]
#=> [2, 4, 6, 8]

Note: how all elements not divisible by 2 have been removed.

(Q) Does python call by reference or call by value?

ANSWER: Be prepared to go down a rabbit hole of semantics if you google this question and read the top few pages.

In a nutshell, all names call by reference, but some memory locations hold objects while others hold pointers to yet other memory locations.

name = 'object'

Let’s see how this works with strings. We’ll instantiate a name and object, point other names to it. Then delete the first name.

x = 'some text'
y = x
x is y #=> Truedel x # this deletes the 'a' name but does nothing to the object in memoryz = y
y is z #=> True

What we see is that all these names point to the same object in memory, which wasn’t affected by del x.

Here’s another interesting example with a function.

name = 'text'def add_chars(str1):
    print( id(str1) ) #=> 4353702856
    print( id(name) ) #=> 4353702856
    # new name, same object
    str2 = str1
    # creates a new name (with same name as the first) AND object
    str1 += 's' 
    print( id(str1) ) #=> 4387143328
    # still the original object
    print( id(str2) ) #=> 4353702856
print(name) #=>text

Notice how adding an s to the string inside the function created a new name AND a new object. Even though the new name has the same “name” as the existing name.

(Q) How to reverse a list?

ANSWER: Note how reverse() is called on the list and mutate it. It doesn’t return the mutated list itself.

li = ['a','b','c']print(li)
#=> ['a', 'b', 'c']
#=> ['c', 'b', 'a']

(Q) How does string multiplication work?

ANSWER: Let’s see the results of multiplying the string ‘cat’ by 3.

'cat' * 3
#=> 'catcatcat'

The string is concatenated to itself 3 times.

(Q) How does list multiplication work?

ANSWER: Let’s see the result of multiplying a list, [1,2,3] by 2.

[1,2,3] * 2
#=> [1, 2, 3, 1, 2, 3]

A list is outputted containing the contents of [1,2,3] repeated twice.

(Q) What does “self” refer to in a class?

ANSWER: Self refers to the instance of the class itself. It’s how we give methods to access to and the ability to update the object they belong to.

Below, passing self to __init__() gives us the ability to set the color of an instance on initialization.

class Shirt:
    def __init__(self, color):
        self.color = color
s = Shirt('yellow')
#=> 'yellow'

(Q) How can you concatenate lists in python?

ANSWER: Adding 2 lists together concatenates them. Note that arrays do not function the same way.

a = [1,2]
b = [3,4,5]a + b
#=> [1, 2, 3, 4, 5]

(Q) What is the difference between a shallow and a deep copy?


i) Reference the original object. This points a new name, li2, to the same place in memory to which li1 points. So any change we make to li1 also occurs to li2.

li1 = [['a'],['b'],['c']]
li2 = li1li1.append(['d'])
#=> [['a'], ['b'], ['c'], ['d']]

ii) Create a shallow copy of the original. We can do this with the list() constructor, or the more pythonic mylist.copy() (thanks

A shallow copy creates a new object but fills it with references to the original. So adding a new object to the original collection, li3, doesn’t propagate to li4, but modifying one of the objects in li3 will propagate to li4.

li3 = [['a'],['b'],['c']]
li4 = list(li3)li3.append([4])
#=> [['a'], ['b'], ['c']]li3[0][0] = ['X']
#=> [[['X']], ['b'], ['c']]

iii) Create a deep copy. This is done with copy.deepcopy(). The 2 objects are now completely independent and changes to either have no effect on the other.

import copyli5 = [['a'],['b'],['c']]
li6 = copy.deepcopy(li5)li5.append([4])
li5[0][0] = ['X']
#=> [['a'], ['b'], ['c']]

(Q) What is the difference between lists and arrays?


  • Lists exist in python’s standard library. Arrays are defined by Numpy.
  • Lists can be populated with different types of data at each index. Arrays require homogeneous elements.
  • Arithmetic on lists adds or removes elements from the list. Arithmetic on arrays functions per linear algebra.
  • Arrays also use less memory and come with significantly more functionality.

(Q) How to concatenate two arrays?

ANSWER: Remember, arrays are not lists. Arrays are from Numpy and arithmetic functions like linear algebra.

We need to use Numpy’s concatenate function to do it.

import numpy as npa = np.array([1,2,3])
b = np.array([4,5,6])np.concatenate((a,b))
#=> array([1, 2, 3, 4, 5, 6])

(Q) What is Python, and what is it used for?

ANSWER: 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).

(Q) List the important features of Python.


  • 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

(Q) What are the different built-in data types in Python?


  • Number (int, float, and complex)
  • String (str)
  • Tuple (tuple)
  • Range (range)
  • List (list)

(Q) Differentiate between %, /, and //?

ANSWER:  % (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.

(Q) 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

print (x(7,8))

Output: 15

Yet another important question in this list of Python data science interview questions. So prepare accordingly.

(Q) 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.

(Q) How do you differentiate between global and local variables?

ANSWER: 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.

(Q) Explain the map, reduce and filter functions.


(Q) Explain how Python data analysis libraries are used and list some common ones.


  • NumPy
  • SciPy
  • TensorFlow
  • SciKit
  • Seaborn

(Q) What is a negative index used for in Python?

ANSWER: 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.

(Q) 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.

(Q) What is PEP8?

ANSWER: 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.

(Q) 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, and 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.

(Q) What are generators and decorators?

ANSWER:  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.

(Q) What is an Eigenvalue and Eigenvector?

ANSWER: Eigenvectors are used for understanding linear transformations. They are the directions along which a particular linear transformation acts by flipping, compressing, or stretching. Eigenvalues can be referred to as the strength of the transformation in the direction of the eigenvector or the factor by which the compression occurs. We usually calculate the eigenvectors for a correlation or covariance matrix in data analysis.

(Q)What is Gradient Descent?

ANSWER: Gradient descent is an iterative procedure that minimizes the cost function parametrized by model parameters. It is an optimization method based on a convex function and trims the parameters iteratively to help the given function attain its local minimum.

  • Gradient measures the change in parameter with respect to the change in error.
  • Imagine a blindfolded person on top of a hill and wanting to reach a lower altitude.
  • The simple technique he can use is to feel the ground in every direction and take a step in the direction where the ground is descending faster.
  • Here we need the help of the learning rate which says the size of the step we take to reach the minimum.
  • The learning rate should be chosen so that it should not be too high or too low.
  • When the selected learning rate is too high, it tends to bounce back and forth between the convex function of the gradient descent, and when it is too low, we will reach the minimum very slowly.

(Q) What do you understand by logistic regression? Explain one of its use-cases.

ANSWER: Logistic regression is one of the most popular machine learning models used for solving a binary classification problem, that is, a problem where the output can take any one of the two possible values. Its equation is given by

logistic regression
Where X represents the feature variable,  a,b are the coefficients, and Y is the target variable. Usually, if the value of Y is greater than some threshold value, the input variable is labeled with class A. Otherwise, it is labeled with class B.

(Q) What is K-means? 

ANSWER: K-means clustering algorithm is an unsupervised machine learning algorithm that classifies a dataset with n observations into k clusters. Each observation is labeled to the cluster with the nearest mean.

(Q)How will you find the right K for K-means?

ANSWER:To find the optimal value for k, one can use the elbow method or the silhouette method.

(Q) What do you understand by feature vectors?

ANSWER: Feature vectors are the set of variables containing values describing each observation’s characteristics in a dataset. These vectors serve as input vectors to a machine learning model.

(Q)How beneficial is dropout regularisation in deep learning models? Does it speed up or slow down the training process, and why?

ANSWER: The dropout regularisation method mostly proves beneficial for cases where the dataset is small, and a deep neural network is likely to overfit during training. The computational factor has to be considered for large datasets, which may outweigh the benefit of dropout regularisation.

The dropout regularisation method involves the random removal of a layer from a deep neural network, which speeds up the training process.

(Q) What do you understand by interpolating and extrapolating the given data?

ANSWER: Interpolating the data means one is estimating the values in between two known values of a variable from the dataset. On the other hand, extrapolating the data means one is estimating the values that lie outside the range of a variable.

(Q) Why would you use enumerate() when iterating on a sequence?

ANSWER:enumerate() allows tracking index when iterating over a sequence. It’s more pythonic than defining and incrementing an integer representing the index.

li = ['a','b','c','d','e']for idx,val in enumerate(li):
    print(idx, val)
#=> 0 a
#=> 1 b
#=> 2 c
#=> 3 d
#=> 4 e

(Q) What is the difference between pass, continue and break?

ANSWER: This for loop.

a = [1,2,3,4,5]
a2 = []
for i in a:
     a2.append(i + 1)print(a2)
#=> [2, 3, 4, 5, 6]


a3 = [i+1 for i in a]print(a3)
#=> [2, 3, 4, 5, 6]

List comprehension is generally accepted as more pythonic where it’s still readable.