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Introduction

Whether you’re applying for a role in data science, machine learning, or Python development, NumPy interview questions are commonly asked during technical rounds. NumPy (Numerical Python) is the foundation for numerical computations in Python. Understanding its concepts and applications is crucial for any aspiring data professional.

In this blog, we cover the most important NumPy interview questions, from basic to advanced, with clear and concise answers. Let’s get started!

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📌 Basic NumPy Interview Questions

1. What is NumPy?

Answer:
NumPy is an open-source Python library used for numerical computations. It provides support for large multidimensional arrays and matrices, along with mathematical functions to operate on them efficiently.

2. How do you install NumPy?

bashCopyEditpip install numpy

You can also use conda install numpy if you’re using Anaconda.

3. What is the difference between a Python list and a NumPy array?

Feature	Python List	NumPy Array
Speed	Slower	Faster
Memory	More	Less
Operations	Manual looping	Vectorized operations

4. How do you create a NumPy array?

pythonCopyEditimport numpy as np
arr = np.array([1, 2, 3])

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⚙️ Intermediate NumPy Interview Questions

5. What is broadcasting in NumPy?

Answer:
Broadcasting allows NumPy to perform arithmetic operations on arrays of different shapes. It automatically expands the smaller array to match the shape of the larger one.

6. What are the main attributes of a NumPy array?

• ndarray.ndim – Number of dimensions
• ndarray.shape – Shape of array (rows, columns)
• ndarray.size – Total number of elements
• ndarray.dtype – Data type of elements

7. How do you generate random numbers in NumPy?

pythonCopyEditnp.random.rand(3)  # Random numbers between 0 and 1
np.random.randint(1, 10, size=(2, 2))  # Random integers

8. Explain the difference between copy() and view() in NumPy.

• copy() creates a new array with its own data.
• view() creates a new array object that shares the same data as the original.

9. What is the use of reshape() in NumPy?

It changes the shape of an array without changing its data.

pythonCopyEditarr = np.array([1, 2, 3, 4])
arr.reshape(2, 2)

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🚀 Advanced NumPy Interview Questions

10. What are universal functions (ufuncs) in NumPy?

Answer:
Universal functions are functions that operate element-wise on arrays, like np.add(), np.subtract(), np.sin(), etc. They are highly optimized and fast.

11. How do you perform matrix multiplication in NumPy?

pythonCopyEditnp.dot(A, B)  # or A @ B

12. How do you find the inverse of a matrix in NumPy?

pythonCopyEditnp.linalg.inv(matrix)

13. How do you handle NaN values in NumPy?

Use np.isnan() to detect and np.nan_to_num() to replace:

pythonCopyEditarr = np.array([1, np.nan, 3])
np.nan_to_num(arr, nan=0)

14. How can NumPy be used for linear algebra?

NumPy provides a sub-package numpy.linalg with methods for:

• Eigenvalues and eigenvectors
• Determinants
• Solving linear equations
• Matrix decomposition

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✅ Practical NumPy Coding Questions

15. Find the mean of an array without using built-in mean function.

pythonCopyEditarr = np.array([1, 2, 3, 4, 5])
mean = np.sum(arr) / arr.size

16. How do you flatten a multidimensional array?

pythonCopyEditarr = np.array([[1, 2], [3, 4]])
flat = arr.flatten()

17. How to filter even numbers from an array?

pythonCopyEditarr = np.array([1, 2, 3, 4, 5, 6])
even = arr[arr % 2 == 0]

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❓ FAQs on NumPy Interview Questions

Q1. Is NumPy enough for data science interviews?
No, it’s a foundational skill. You should also know Pandas, Matplotlib, and Scikit-learn.

Q2. Are NumPy questions asked in FAANG interviews?
Not directly, but strong knowledge of NumPy can help in coding rounds or ML system design.

Q3. Can I use NumPy in real-time applications?
Yes. NumPy is used in real-time systems for simulations, ML model training, and more.