Most Frequently Asked NumPy Interview Questions and Answers [For Freshers]

If you are looking to have a glorious career in the technological sphere, you already know that a qualification in NumPy is one of the most sought-after skills out there. After all, NumPy is built on the de facto standards of computing arrays. 

NumPy is one of the commonly used libraries of Python for working with arrays. It is broadly used for performing the vast majority of advanced mathematical calculations on a large scale of data. The NumPy arrays are much faster and more compact than Python lists.

There are various advantages of using NumPy as well such as the utilization of lesser storage space. This lesser storage space allows the users to specify the data types. The feature of specifying the data type allows the further optimization of code.

A common apprehension is that “Why should we use NumPy rather than Matlab, octave or yorick?” To answer, NumPy supports the operations on arrays of homogenous data. This makes Python act as a really advanced programming language that manipulates numerical data. It increases the functionality and operability of NumPy.

Although many relevant questions have been discussed in the article a few basic things should also be known in case the interviewer asks during the NumPy coding questions.

  1. Arrays- Arrays in NumPy are a grid of values. All of these values are of the same type. 
  2. Function in NumPy-  Some of the functions are mentioned below-
  1. numpy.linspace
  2. numpy.digitize
  3. numpy.random
  4. Numpy.nan
  5. numpy.repeat

Sometimes the interviewer can also ask about the founding year of NumPy, one should be prepared with a brief answer. This can be asked even during numpy interview questions for data science.  NumPy was created in the year 2005 by Travis Oliphant.

So, here’s a listing of some commonly asked NumPy interview questions and answers you might want to look up before you appear for your next interview. 

Top 15 NumPy Interview Questions and Answers

Question 1: What is NumPy?

NumPy is an open-source, versatile general-purpose package used for array-processing. It is short on Numerical Python. It is known for its high-end performance with powerful N-dimensional array objects and the tools it is loaded with to work with arrays. The package is an extension of Python and is used to perform scientific computations and other broadcasting functions.

NumPy is easy to use, well-optimized, and highly flexible. It is compared with MATLAB on the basis of their functionalities as both of them facilitate writing fast programs as long as most of the functions work on the arrays. NumPy is closely integrated with Python and makes it a much more sophisticated programming language.

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Question 2: What are the uses of NumPy?

The open-source numerical library on Python supports multi-dimensional arrays and contains matrix data structures. Different types of mathematical operations can be performed on arrays using NumPy. This includes trigonometric operations as well as statistical and algebraic computations. Numeric and Numarray are extensions of NumPy. 

Another answer for NumPy data science interview questions could be – “NumPy is used for scientific computing, deep learning, and financial analysis. Various functions can be performed with the aid of NumPy such as the arithmetic operations, stacking, matrix operations, broadcasting, linear algebra, etc.”

Question 3: Why is NumPy preferred to other programming tools such as IDL, Matlab, Octave, Or Yorick?

NumPy is a high-performance library in the Python programming language that allows scientific calculations. It is preferred to Idl, Matlab, Octave, Or Yorick because it is open-source and free. Also, since it uses Python which is a general-purpose programming language, it scores over a generic programming language when it comes to connecting Python’s interpreter to C/C++ and Fortran code. 

NumPy supports multi-dimensional arrays and matrices and helps to perform complex mathematical operations on them. 

Question 4: What are the various features of NumPy?

As a powerful open-source package used for array-processing, NumPy has various useful features. They are:

  1. Contains an N-dimensional array object
  2. It is  interoperable; compatible with many hardware and computing platforms
  3. Works extremely well with array libraries; sparse, distributed or GPU
  4. Ability to perform complicated (broadcasting) functions
  5. Tools that enable integration with C or C++ and Fortran code 
  6. Ability to perform high-level mathematical functions like statistics, Fourier transform, sorting, searching, linear algebra, etc 
  7. It can also behave as a multi-dimensional container for generic data
  8. Supports scientific and financial calculations.
  9. Can work with various types of databases
  10. Provides multi-dimensional arrays
  11. Indexing, Slicing, or Masking with other arrays facilitate sin accessing the specific pixels of an image.

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Question 5: How can you Install NumPy on Windows?

To install NumPy on Windows, you must first download and install Python on your computer.

Follow the steps given below to install Python: 

Step 1: Visit the official page of Python and download Python and Python executable binaries on your Windows 10/8/7

Step 2: Open Python executable installer and press Run

Step 3: Install pip on your Windows system

Using pip, you can install NumPy in Python. Below is the Installation Process of NumPy: 

Step 1: Start the terminal

Step 2: Type pip 

Step 3: install NumPy

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Question 6. List the advantages NumPy Arrays have over (nested) Python lists?

Python’s lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. 

They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done. Also, each iteration would have to undergo type checks and require Python API bookkeeping resulting in very few operations being carried by C loops. 

This makes for one of the commonly asked numpy questions, where the advantages are required to enlist. Another advantage could be the less memory space that is utilized to store the data which helps in further optimization of the code. Scientific computing and array-oriented computing are more aligned advantages of NumPy.

Question 7: List the steps to create a 1D array and 2D array

A one-dimensional array is created as follows: 


num = np.array(num)

print(“1d array : “,num) 

A two-dimensional array is created as follows: 


num2 = np.array(num2)

print(“\n2d array : “,num2)

A 1-D array stands for a one-dimensional array that creates the array in one dimension. Whereas the 2D arrays have a collection of rows and columns.

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Question 8: How do you create a 3D array?

A three-dimensional array is created as follows: 


num3 = np.array(num3)

print(“\n3d array : “,num3)

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Question 9: What are the steps to use shape for a 1D array, 2D array and 3D/ND array respectively?

1D Array:

num=[1,2,3] if not added

print(‘\nshpae of 1d ‘,num.shape)

2D Array:

num2=[[1,2,3],[4,5,6]] if not added

print(‘\nshpae of 2d ‘,num2.shape)

3D or ND Array: 

num3=[[[1,2,3],[4,5,6],[7,8,9]]] if not added

print(‘\nshpae of 3d ‘,num3.shape)

Question 10: How can you identify the datatype of a given NumPy array?

Use the following sequence of codes to identify the datatype of a NumPy array. 

print(‘\n data type num 1 ‘,num.dtype)

print(‘\n data type num 2 ‘,num2.dtype)

print(‘\n data type num 3 ‘,num3.dtype)

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Question 11. What is the procedure to count the number of times a given value appears in an array of integers?

You can count the number of times a given value appears using the bincount() function. It should be noted that the bincount() function accepts positive integers or boolean expressions as its argument. Negative integers cannot be used. 

Use NumPy.bincount(). The resulting array is

>>> arr = NumPy.array([0, 5, 4, 0, 4, 4, 3, 0, 0, 5, 2, 1, 1, 9])

>> NumPy.bincount(arr)


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Question 12. How do you check for an empty (zero Element) array?

If the variable is an array, you can check for an empty array by using the size attribute. However, it is possible that the variable is a list or a sequence type, in that case, you can use len().

The preferable way to check for a zero element is the size attribute. This is because: 

>>> a = NumPy.zeros((1,0))

>>> a.size



>>> len(a)


Question 13: What is the procedure to find the indices of an array on NumPy where some condition is true?

You may use the function numpy.nonzero() to find the indices or an array. You can also use the nonzero() method to do so. 

In the following program, we will take an array a, where the condition is a > 3. It returns a boolean array. We know False on Python and NumPy is denoted as 0. Therefore, np.nonzero(a > 3) will return the indices of the array a where the condition is True. 

>>> import numpy as np

>>> a = np.array([[1,2,3],[4,5,6],[7,8,9]])

>>> a > 3

array([[False, False, False],

       [ True,  True,  True],

       [ True,  True,  True]], dtype=bool)

>>> np.nonzero(a > 3)

(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))

You can also call the nonzero() method of the boolean array.

>>> (a > 3).nonzero()

(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))

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Question 14: Shown below is the input NumPy array. Delete column two and replace it with the new column given below.

import NumPy

sampleArray = NumPy.array([[34,43,73],[82,22,12],[53,94,66]]) 

newColumn = NumPy.array([[10,10,10]])
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Expected Output:

Printing Original array

[[34 43 73]

 [82 22 12]

 [53 94 66]]

Array after deleting column 2 on axis 1

[[34 73]

 [82 12]

 [53 66]]

Array after inserting column 2 on axis 1

[[34 10 73]

 [82 10 12]

 [53 10 66]]


import NumPy

print(“Printing Original array”)

sampleArray = NumPy.array([[34,43,73],[82,22,12],[53,94,66]]) 

print (sampleArray)

print(“Array after deleting column 2 on axis 1”)

sampleArray = NumPy.delete(sampleArray , 1, axis = 1) 

print (sampleArray)

arr = NumPy.array([[10,10,10]])

print(“Array after inserting column 2 on axis 1”)

sampleArray = NumPy.insert(sampleArray , 1, arr, axis = 1) 

print (sampleArray)

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Question 15: Create a two 2-D array. Plot it using matplotlib


import NumPy

print(“Printing Original array”)

sampleArray = NumPy.array([[34,43,73],[82,22,12],[53,94,66]]) 

print (sampleArray)

print(“Array after deleting column 2 on axis 1”)

sampleArray = NumPy.delete(sampleArray , 1, axis = 1) 

print (sampleArray)

arr = NumPy.array([[10,10,10]])

print(“Array after inserting column 2 on axis 1”)

sampleArray = NumPy.insert(sampleArray , 1, arr, axis = 1) 

print (sampleArray)


We hope the above-mentioned NumPy interview questions will help you prepare for your upcoming interview sessions. If you are looking for courses that can help you get a hold of Python language, upGrad can be the best platform. 

If you are curious to learn about data science, check out IIIT-B & upGrad’s Online Data Science Programs which are created for working professionals and offer 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

 We hope this helps. Good luck for with your Interview!

How do I practice NumPy?

Going through a step-by-step procedure can make any topic easy to learn. By performing a few basic exercises, you will get a grip of the library and also understand its usage. Firstly, begin with the installation process of the NumPy library in your system. Later on, continue with the below exercises that are recommended for beginners to practice NumPy: Addition of 2 NumPy arrays Multiplying a NumPy array with a scalar Identity matrix Array re-dimensioning Array datatype conversion Obtaining Boolean array from any Binary array Horizontal stacking of NumPy arrays Generation of custom sequences With the help of these tasks, you will be able to practice NumPy and get the hang of it. These are some of the basics that will help you to get a command over the same.

Why is NumPy so fast?

NumPy is considered to be faster than other Python libraries. The main reasons behind the extremely fast speed of NumPy are: NumPy arrays are formed only with a collection of elements that have similar data-types. These elements are all densely packed in memory. On the other hand, a Python list can consist of different data-types. Due to this reason, there are plenty of constraints while computing Python lists. NumPy can divide a single task into several sub-tasks and process all of them in parallel. All the functions of NumPy are implemented in C. This is another reason why the processing becomes faster in NumPy as compared to the Python lists.

Should I use Pandas or NumPy?

If you are a data scientist, then both pandas and NumPy are essential tools for you in Python. Both the libraries have their own set of benefits. If you want efficient vector and matrix operations, then NumPy is the best option to go with. At the same time, Pandas efficiently provides R-like data frames to allow the users to receive intuitive tabular data analysis. Based on several tests by developers, it has been seen that NumPy is more optimized when it comes to arithmetic computations. Other than that, NumPy is memory efficient compared to Pandas, while Pandas are better performing when there are 500K or more rows to deal with. So, one can say that the usage of both libraries will completely depend upon your usage.

How many dimensions can a NumPy array have?

NumPy arrays can have more than one dimension.

What is the difference between NumPy and Pandas?

NumPy has homogenous data whereas Pandas can have different types of data. NumPy has multiple dimensions whereas Pandas can be two-dimensional. NumPy is faster and Pandas is a little slower.

How do I practice NumPy?

The beginners can practice NumPy by being strategic in their approach, such as- Element-wise addition of 2 NumPy arrays. Multiplication of matrix (NumPy array) by a scalar Conversion of array data type Sequence generation

What is a NumPy array?

A NumPy array is an N-dimensional array that is used for various elements such as linear algebra, Fourier transform, etc. This is array is arranged in a grid manner, this grid has values of the homogenous type.

What is the difference between a NumPy array and a Python array?

The size at the time of the creation of arrays of NumPy is fixed, which is not the same for Python lists.NumPy arrays have a homogenous data type whereas the Python arrays do not have a homogenous but rather heterogeneous data type. NumPy arrays have many functions, elements, and operations for complex computation.

Is NumPy a module or library?

NumPy is a library for Python that is sued to work with arrays. It has various functions that allow working with linear algebra, Fourier transform, etc.

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