Top 15 NumPy Viva Important Questions with Code Examples (2026 Guide)
If you are preparing for Python programming viva, practical exams, lab tests, or technical interviews, then learning NumPy viva important questions is very important. NumPy is one of the most commonly asked topics in BCA, MCA, B.Tech, and coding interviews because it is the foundation of data science, machine learning, and scientific computing in Python.
In this complete guide, we will cover the top NumPy viva important questions with answers and code examples for quick revision and better understanding.
If you want to score better in exams and crack technical interviews, this article will help you master the basics of NumPy easily.
What is NumPy in Python?
NumPy stands for Numerical Python. It is a powerful Python library used for:
- Working with arrays
- Mathematical calculations
- Linear algebra
- Scientific operations
- Data science and machine learning
NumPy is faster than normal Python lists because it is optimized for numerical computation.
Example Code
import numpy as nparr = np.array([1, 2, 3, 4])
print(arr)
Output
[1 2 3 4]
1. Why is NumPy Used in Python?
NumPy is used because:
- It performs fast mathematical operations
- It supports multi-dimensional arrays
- It consumes less memory
- It is useful in machine learning and data science
- It works well with Pandas and Matplotlib
- It improves performance compared to Python lists
Example Code
import numpy as npa = np.array([10, 20, 30])
print(a * 2)
Output
[20 40 60]
2. What is an Array in NumPy?
An array is a collection of elements of the same data type stored together.
Unlike Python lists, NumPy arrays are faster and more memory efficient.
Example Code
import numpy as npa = np.array([10, 20, 30, 40])
print(a)
3. Difference Between Python List and NumPy Array
This is one of the most common NumPy viva questions.
Python List
- Slower
- Uses more memory
- Supports mixed data types
- Not ideal for heavy mathematical operations
NumPy Array
- Faster
- Uses less memory
- Usually same data type
- Best for mathematical operations
Example Code
list1 = [1, 2, 3]import numpy as np
arr1 = np.array([1, 2, 3])print(list1)
print(arr1)
4. How to Install NumPy?
Use this command in terminal or command prompt:
pip install numpy
This command installs the NumPy library in Python.
This is one of the most basic but important viva questions.
5. How to Import NumPy?
The standard method is:
import numpy as np
Here, np is the commonly used short alias for NumPy.
This makes coding easier and cleaner.
6. What is ndarray in NumPy?
ndarray means N-dimensional array.
It is the main object in NumPy used to store data.
Example Code
import numpy as nparr = np.array([1, 2, 3])
print(type(arr))
Output
<class 'numpy.ndarray'>
7. Difference Between 1D, 2D, and 3D Arrays
This is a very common practical viva question.
1D Array Example
import numpy as nparr1 = np.array([1, 2, 3])
print(arr1)
2D Array Example
arr2 = np.array([[1, 2], [3, 4]])
print(arr2)
3D Array Example
arr3 = np.array([[[1, 2], [3, 4]]])
print(arr3)
8. What is Shape of an Array?
The shape tells the number of rows and columns in an array.
Example Code
import numpy as nparr = np.array([[1, 2], [3, 4]])
print(arr.shape)
Output
(2, 2)
This means:
- 2 rows
- 2 columns
9. What is Slicing in NumPy?
Slicing is used to access specific parts of an array.
Example Code
import numpy as nparr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])
Output
[20 30 40]
Slicing is useful in data analysis and machine learning.
10. What is Broadcasting in NumPy?
Broadcasting allows operations between arrays of different shapes.
Example Code
import numpy as nparr = np.array([1, 2, 3])
print(arr + 5)
Output
[6 7 8]
Here, 5 is automatically added to every element.
This saves time and improves performance.
11. What is zeros() in NumPy?
The zeros() function creates an array filled with zeros.
Example Code
import numpy as npprint(np.zeros((2, 3)))
Output
[[0. 0. 0.]
[0. 0. 0.]]
Used when initializing empty values.
12. What is ones() in NumPy?
The ones() function creates an array filled with ones.
Example Code
import numpy as npprint(np.ones((2, 2)))
Output
[[1. 1.]
[1. 1.]]
Very useful for matrix operations.
13. What is arange() in NumPy?
The arange() function creates an array using a range of values.
Example Code
import numpy as npprint(np.arange(1, 10, 2))
Output
[1 3 5 7 9]
It works like Python’s range() but returns a NumPy array.
14. What is reshape() in NumPy?
The reshape() function changes the shape of an array without changing its data.
Example Code
import numpy as nparr = np.array([1, 2, 3, 4, 5, 6])
print(arr.reshape(2, 3))
Output
[[1 2 3]
[4 5 6]]
This is very important in machine learning preprocessing.
15. What is linspace() in NumPy?
The linspace() function creates evenly spaced values between two numbers.
Example Code
import numpy as npprint(np.linspace(1, 10, 5))
Output
[ 1. 3.25 5.5 7.75 10. ]
Used in graphs, plotting, and scientific calculations.
NumPy Viva Important Question for Exams and Interviews
Here are some extra quick viva questions:
Is NumPy better than Python List?
Yes, NumPy is faster, uses less memory, and supports advanced mathematical operations.
Can NumPy store mixed data types?
Usually NumPy works best with the same data type for better performance.
Is NumPy used in Machine Learning?
Yes, NumPy is the foundation of machine learning and data science.
What is the full form of NumPy?
Numerical Python.
Final Conclusion
These are the top NumPy viva important questions with answers and code examples commonly asked in practical exams, lab tests, and interviews.
If you prepare these NumPy viva questions properly, you can confidently answer both theory and coding-based questions.
Instead of only memorizing answers, practice the code examples and understand the concepts deeply.
Strong NumPy basics will help you learn:
- Pandas
- Machine Learning
- Artificial Intelligence
- Data Science
- Deep Learning
If you want success in Python interviews and practical exams, mastering NumPy is the first step.
These NumPy viva important question topics are very useful for students preparing for practical exams and technical interviews. If you practice every NumPy viva important question, you can improve your confidence and score better in viva exams.
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Learn more from the official NumPy documentation:
https://numpy.org/doc/