70 Must-Know NumPy Interview Questions and Answers 2024

NumPy Fundamentals

1.
What is NumPy, and why is it important in Machine Learning?
Answer:
2.
Explain how NumPy arrays are different from Python lists.
Answer:
3.
What are the main attributes of a NumPy ndarray?
Answer:
4.
How do you create a NumPy array from a regular Python list?
Answer:
5.
Explain the concept of broadcasting in NumPy.
Answer:
6.
What are the data types supported by NumPy arrays?
Answer:
7.
How do you inspect the shape and size of a NumPy array?
Answer:
8.
What is the difference between a deep copy and a shallow copy in NumPy?
Answer:
9.
How do you perform element-wise operations in NumPy?
Answer:
10.
What are universal functions (ufuncs) in NumPy?
Answer:

Array Operations

11.
How do you perform matrix multiplication using NumPy?
Answer:
12.
Explain how to invert a matrix in NumPy.
Answer:
13.
How do you calculate the determinant of a matrix?
Answer:
14.
What is the use of the _axis_ parameter in NumPy functions?
Answer:
15.
How do you concatenate two arrays in NumPy?
Answer:
16.
Describe how you would flatten a multi-dimensional array.
Answer:
17.
How do you calculate the eigenvalues and eigenvectors of a matrix in NumPy?
Answer:
18.
Explain the use of slicing and indexing with NumPy arrays.
Answer:
19.
How can you reverse an array in NumPy?
Answer:
20.
How do you apply a conditional filter to a NumPy array?
Answer:

Statistical Analysis

21.
How do you compute the mean, median, and standard deviation with NumPy?
Answer:
22.
Explain how to generate random data with NumPy.
Answer:
23.
What is the purpose of the NumPy histogram function?
Answer:
24.
How do you normalize an array in NumPy?
Answer:
25.
How can you compute percentiles with NumPy?
Answer:
26.
What is the difference between _np.var()_ and _np.std()_?
Answer:
27.
How do you calculate the correlation coefficient using NumPy?
Answer:
28.
Explain the use of the np.cumsum() and np.cumprod() functions.
Answer:

Memory and Performance Considerations

29.
Why is NumPy more efficient for numerical computations than pure Python?
Answer:
30.
What is the concept of vectorization in NumPy?
Answer:
31.
Explain the term “stride” in the context of NumPy arrays.
Answer:
32.
How do you check the memory size of a NumPy array?
Answer:
33.
Discuss the performance benefits of using NumPy’s in-place operations.
Answer:
34.
How does NumPy handle data types to optimize memory use?
Answer:

Advanced Array Manipulation

35.
What are NumPy strides, and how do they affect array manipulation?
Answer:
36.
How do you create a record array in NumPy?
Answer:
37.
Explain the concept and use of masked arrays in NumPy.
Answer:
38.
How do you stack multiple arrays vertically and horizontally?
Answer:
39.
Describe the process for creating a structured array in NumPy.
Answer:
40.
How do you save and load NumPy arrays to and from disk?
Answer:
41.
What are the functions available for padding arrays in NumPy?
Answer:

Coding Challenges

42.
Write a NumPy code to create a 3x3 identity matrix.
Answer:
43.
Code a function in NumPy to compute the moving average of a 1D array.
Answer:
44.
Generate a 2D NumPy array of random integers and normalize it between 0 and 1.
Answer:
45.
Create a NumPy code snippet to extract all odd numbers from an array.
Answer:
46.
Implement a routine to calculate the outer product of two vectors in NumPy.
Answer:
47.
Write a NumPy program to create a checkerboard 8x8 matrix using the _tile_ function.
Answer:
48.
Code a NumPy snippet to create a border around an existing array.
Answer:
49.
Write a function to compute the convolution of two matrices in NumPy.
Answer:
50.
Implement a script that computes the Fibonacci sequence using a NumPy matrix.
Answer:
51.
Write a code to replace all elements greater than a certain threshold in a NumPy array with a specific value.
Answer:
52.
Implement an efficient rolling window calculation for a 1D array using NumPy.
Answer:

Practical Applications

53.
How would you use NumPy to process image data for a convolutional neural network?
Answer:
54.
Describe how you can use NumPy for simulating Monte Carlo experiments.
Answer:
55.
How can NumPy be used for audio signal processing?
Answer:
56.
Explain how you would implement gradient descent optimization with NumPy.
Answer:
57.
Discuss the role of NumPy in managing data for training a machine learning model.
Answer:

Troubleshooting and Problem Solving

58.
How do you handle NaN or infinite values in a NumPy array?
Answer:
59.
What methods are there in NumPy to deal with missing data?
Answer:
60.
How do you find unique values and their counts in a NumPy array?
Answer:
61.
Discuss the potential issues when importing large datasets into NumPy arrays.
Answer:
62.
Explain how to resolve the MemoryError when working with very large arrays in NumPy.
Answer:

Advanced Topics and NumPy Internals

63.
What are NumPy “polynomial” objects and how are they used?
Answer:
64.
How does the internal C-API contribute to NumPy’s performance?
Answer:
65.
Explain the concept of a stride trick in NumPy.
Answer:
66.
Discuss the use of NumPy for operations on polynomials.
Answer:
67.
What is the role of the NumPy nditer object?
Answer:

External Integration and Extensions

68.
Explain how NumPy integrates with other Python libraries like Pandas and Matplotlib.
Answer:
69.
How can you use NumPy arrays with Cython for performance optimization?
Answer:
70.
Describe how NumPy can be used with JAX for accelerated machine learning computation.
Answer:

Ace your next tech interview with confidence

Explore our carefully curated catalog of interview essentials covering full-stack, data structures and algorithms, system design, data science, and machine learning interview questions

70 NumPy interview questions

What is NumPy, and why is it important in Machine Learning?

Explain how NumPy arrays are different from Python lists.

What are the main attributes of a NumPy ndarray?

How do you create a NumPy array from a regular Python list?

Explain the concept of broadcasting in NumPy.

What are the data types supported by NumPy arrays?

How do you inspect the shape and size of a NumPy array?

What is the difference between a deep copy and a shallow copy in NumPy?

How do you perform element-wise operations in NumPy?

What are universal functions (ufuncs) in NumPy?

How do you perform matrix multiplication using NumPy?

Explain how to invert a matrix in NumPy.

How do you calculate the determinant of a matrix?

What is the use of the _axis_ parameter in NumPy functions?

How do you concatenate two arrays in NumPy?

Describe how you would flatten a multi-dimensional array.

How do you calculate the eigenvalues and eigenvectors of a matrix in NumPy?

Explain the use of slicing and indexing with NumPy arrays.

How can you reverse an array in NumPy?

How do you apply a conditional filter to a NumPy array?

How do you compute the mean, median, and standard deviation with NumPy?

Explain how to generate random data with NumPy.

What is the purpose of the NumPy histogram function?

How do you normalize an array in NumPy?

How can you compute percentiles with NumPy?

What is the difference between _np.var()_ and _np.std()_?

How do you calculate the correlation coefficient using NumPy?

Explain the use of the np.cumsum() and np.cumprod() functions.

Why is NumPy more efficient for numerical computations than pure Python?

What is the concept of vectorization in NumPy?

Explain the term “stride” in the context of NumPy arrays.

How do you check the memory size of a NumPy array?

Discuss the performance benefits of using NumPy’s in-place operations.

How does NumPy handle data types to optimize memory use?

What are NumPy strides, and how do they affect array manipulation?

How do you create a record array in NumPy?

Explain the concept and use of masked arrays in NumPy.

How do you stack multiple arrays vertically and horizontally?

Describe the process for creating a structured array in NumPy.

How do you save and load NumPy arrays to and from disk?

What are the functions available for padding arrays in NumPy?

Write a NumPy code to create a 3x3 identity matrix.

Code a function in NumPy to compute the moving average of a 1D array.

Generate a 2D NumPy array of random integers and normalize it between 0 and 1.

Create a NumPy code snippet to extract all odd numbers from an array.

Implement a routine to calculate the outer product of two vectors in NumPy.

Write a NumPy program to create a checkerboard 8x8 matrix using the _tile_ function.

Code a NumPy snippet to create a border around an existing array.

Write a function to compute the convolution of two matrices in NumPy.

Implement a script that computes the Fibonacci sequence using a NumPy matrix.

Write a code to replace all elements greater than a certain threshold in a NumPy array with a specific value.

Implement an efficient rolling window calculation for a 1D array using NumPy.

How would you use NumPy to process image data for a convolutional neural network?

Describe how you can use NumPy for simulating Monte Carlo experiments.

How can NumPy be used for audio signal processing?

Explain how you would implement gradient descent optimization with NumPy.

Discuss the role of NumPy in managing data for training a machine learning model.

How do you handle NaN or infinite values in a NumPy array?

What methods are there in NumPy to deal with missing data?

How do you find unique values and their counts in a NumPy array?

Discuss the potential issues when importing large datasets into NumPy arrays.

Explain how to resolve the MemoryError when working with very large arrays in NumPy.

What are NumPy “polynomial” objects and how are they used?

How does the internal C-API contribute to NumPy’s performance?

Explain the concept of a stride trick in NumPy.

Discuss the use of NumPy for operations on polynomials.

What is the role of the NumPy nditer object?

Explain how NumPy integrates with other Python libraries like Pandas and Matplotlib.

How can you use NumPy arrays with Cython for performance optimization?

Describe how NumPy can be used with JAX for accelerated machine learning computation.

Unlock interview insights

Track progress

Save time

Stand out and get your dream job

What is the use of the `_axis_` parameter in NumPy functions?

What is the purpose of the NumPy `histogram` function?

What is the difference between `_np.var()`_ and `_np.std()`_?

Write a NumPy program to create a checkerboard 8x8 matrix using the `_tile_` function.

What is the role of the NumPy `nditer` object?