Whether you’re interviewing for a role in data science, machine learning, or Python development, chances are you’ll face NumPy interview questions. NumPy (Numerical Python) is one of the most important libraries in Python’s data ecosystem. It powers everything from scientific computing to machine learning workflows.
In this article, you’ll find a curated list of 20 essential NumPy interview questions—from basic syntax to advanced operations—each with clear answers and code examples.
🔹 Basic NumPy Interview Questions
1. What is NumPy?
Answer:
NumPy is an open-source Python library used for high-performance numerical operations. It supports multi-dimensional arrays and provides tools for mathematical, logical, statistical, and algebraic operations.
2. How do you install NumPy?
bashCopyEditpip install numpy
# Or using Anaconda
conda install numpy
3. What is the difference between a Python list and a NumPy array?
| Feature | Python List | NumPy Array |
|---|---|---|
| Speed | Slower | Much Faster |
| Memory Usage | Higher | Lower |
| Functionality | Limited | Vectorized Ops |
4. How do you create a NumPy array?
pythonCopyEditimport numpy as np
arr = np.array([1, 2, 3])
⚙️ Intermediate NumPy Interview Questions
5. What is broadcasting?
Answer:
Broadcasting is NumPy’s ability to perform operations on arrays of different shapes without explicit looping. It simplifies code and increases performance.
6. What are the main attributes of a NumPy array?
ndarray.ndim– Number of dimensionsndarray.shape– Shape of the arrayndarray.size– Total number of elementsndarray.dtype– Data type of elements
7. How do you generate random numbers in NumPy?
pythonCopyEditnp.random.rand(3) # Random floats
np.random.randint(1, 10, (2,2)) # Random integers
8. What is the difference between copy() and view()?
copy()– Returns a new array with its own dataview()– Creates a new view that shares the data with the original array
9. What is the use of reshape()?
pythonCopyEditarr = np.array([1, 2, 3, 4])
arr.reshape(2, 2)
It changes the shape of an array without altering the data.
🚀 Advanced NumPy Interview Questions
10. What are universal functions (ufuncs)?
Answer:
Ufuncs operate element-wise on arrays and are optimized for performance. Examples: np.add(), np.sin(), np.sqrt()
11. How do you perform matrix multiplication?
pythonCopyEditnp.dot(A, B) # Or simply A @ B
12. How to find the inverse of a matrix?
pythonCopyEditnp.linalg.inv(matrix)
13. How do you handle NaN values?
pythonCopyEditarr = np.array([1, np.nan, 3])
np.isnan(arr) # Detect
np.nan_to_num(arr) # Replace
14. How is NumPy used for linear algebra?
Answer:
NumPy’s linalg module provides:
- Solving systems of linear equations
- Matrix inversions
- Eigenvalues and eigenvectors
- Singular value decomposition
✅ Practical NumPy Coding Questions
15. Find the mean without using np.mean():
pythonCopyEditarr = np.array([1, 2, 3, 4, 5])
mean = np.sum(arr) / arr.size
16. Flatten a multidimensional array:
pythonCopyEditarr = np.array([[1, 2], [3, 4]])
flat = arr.flatten()
17. Filter even numbers from an array:
pythonCopyEditarr = np.array([1, 2, 3, 4, 5, 6])
even = arr[arr % 2 == 0]
🧠 More Useful NumPy Questions (Bonus)
18. How to stack two arrays vertically and horizontally?
pythonCopyEditnp.vstack((arr1, arr2))
np.hstack((arr1, arr2))
19. How to find unique elements in an array?
pythonCopyEditnp.unique(arr)
20. How do you save and load a NumPy array?
pythonCopyEditnp.save('array.npy', arr)
loaded = np.load('array.npy')
❓ FAQs on NumPy Interview Questions
Q1. Is NumPy enough for data science interviews?
Not entirely. NumPy is foundational, but also prepare for Pandas, Scikit-learn, and Matplotlib.
Q2. Are NumPy questions asked in FAANG interviews?
Not directly, but strong NumPy skills help in solving matrix, array, and ML pipeline questions.
Q3. Can I use NumPy in real-time applications?
Yes. NumPy is widely used in real-time analytics, AI pipelines, and production-ready APIs via libraries like TensorFlow and PyTorch.
📌 Final Thoughts
Mastering NumPy interview questions is crucial if you’re aiming for roles in data science, machine learning, or Python development. Focus not just on syntax but also on real-world application.
For more such interview guides, explore related posts in our Python Interview and Data Analytics & Business Intelligence categories.
