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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. 

So, here’s listing 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 of 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. 

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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. 

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 a N-dimensional array object
  2. It is interolerable; 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

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

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. 

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

A one-dimensional array is created as follows: 

num=[1,2,3]

num = np.array(num)

print(“1d array : “,num) 

A two-dimensional array is created as follows: 

num2=[[1,2,3],[4,5,6]]

num2 = np.array(num2)

print(“\n2d array : “,num2)

Check out: Data Science Interview Questions

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

A three-dimensional array is created as follows: 

num3=[[[1,2,3],[4,5,6],[7,8,9]]]

num3 = np.array(num3)

print(“\n3d array : “,num3)

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)

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 the 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)

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

0

whereas

>>> len(a)

1

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]))

Read: Dataframe in Apache PySpark: Comprehensive Tutorial

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]])

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]]

Solution:

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

Solution:

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)

Conclusion

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 PG Diploma in Data Science which is created for working professionals and offers 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 your Interview!

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