Top 70+ NumPy Interview Questions & Answers: From Basics to Advanced

NumPy Interview Questions and Answers for Freshers

1.   How do you install NumPy?

You can install NumPy using pip, the Python package manager. Open a terminal or

command prompt and run:

pip install numpy

For specific versions:

pip install numpy==1.21.0

To verify the installation:

import numpy as np

print(np.__version__)

2.   How do I create a NumPy array?

You can create a NumPy array using the np.array() function.

Example:

Other methods:

• Using np.zeros((2, 2)) for an array of zeros.
• Using np.ones((3, 3)) for an array of ones.
• Using np.arange(0, 10, 2) for a range of numbers.

3.   What is the purpose of NumPy in Python?

• Efficient Data Handling: When you want a perfect data handling tool, NumPy offers you whole array manipulations, which are not just fast but also optimised for memory usage.
• Integration: Libraries like Pandas, SciPy and Matplotlib are well-integrated for perfect use.
• Operations in Maths: This also covers linear algebra functions, Fourier transform functions, and functions to generate random numbers.

4.   Can you distinguish between single precision and double precision when you are using a NumPy array?

5.   You have to find the dot product of two arrays. Find it with the help of NumPy.

Use the np.dot() function.

Example:

For matrices:

6.   What are the main features of NumPy?

• N-dimensional Array: Supports multi-dimensional arrays.
• Mathematical Functions: Includes operations for linear algebra, statistics, and more.
• Broadcasting: Performs operations on arrays of different shapes.
• Integration: Can interface with C, C++, and Fortran for performance.

7.   Why is NumPy popular in data science and machine learning?

• Performance: Faster than Python lists for numerical operations.
• Library Support: Essential for libraries like TensorFlow and Pandas.
• Ease of Use: Simplifies tasks like matrix operations, statistical analysis, and array manipulation.

8.   What are some common data types supported by NumPy?

Example:

9.   Can you distinguish between a shallow copy and a deep copy when you are using the NumPy library?

Example:

10.  How do I access elements in a NumPy array?

• Single Element: Use indexing.

arr = np.array([10, 20, 30])

print(arr[1])  # Output: 20

• Slices:

print(arr[1:])  # Output: [20, 30]

• Multidimensional:

matrix = np.array([[1, 2], [3, 4]])

print(matrix[1, 0])  # Output: 3

11.  Define the min and max function in NumPy.

• min(): Returns the smallest value.
• max(): Returns the largest value.

Example:

For multidimensional arrays:

matrix = np.array([[1, 2], [3, 4]])

print(np.min(matrix, axis=0))  # Output: [1, 2]

print(np.max(matrix, axis=1))  # Output: [2, 4]

12.  What is the distinction between np.dot() and np.matmul()?

13. Define the mean function in NumPy.

The np.mean() function returns the mean of the elements in an array.

Example:

For multidimensional arrays:

14.  How are NumPy Arrays better than Lists in Python?

15. How to perform element-wise operations on NumPy arrays?

Use arithmetic operators directly:

16. Define the var function in NumPy.

The np.var() function calculates the variance of elements in an array.

Example:

17. What is the significance of the random module in NumPy?

The np.random module generates random numbers and performs sampling operations:

• Random numbers:random.rand()
• Normal distribution:random.randn()
• Integers:random.randint(1, 10)

Example:

18. Convert a multidimensional array to a 1D array.

Use the flatten() or ravel() function:

19. How can you create a NumPy array from a Python list?

20.  What is Matrix Inversion in NumPy?

Matrix inversion is finding a matrix A⁻¹ such that A * A⁻¹ = I, where I is the identity matrix.

Use np.linalg.inv():

21.  Write a NumPy code snippet to create an array of zeros.

Output:

22. How can you concatenate two NumPy arrays vertically?

Use np.vstack():

# Output:

23. How can you create an array with the same values?

Use np.full():

# Output:

Use np.ones() or np.zeros():

ones = np.ones((2, 2))

zeros = np.zeros((2, 2))

24. Your task is to compute the Fourier transformation in a signal. Write the procedure for the same using NumPy.

You can calculate a signal’s Fourier transform with NumPy’s np.fft.fft() function. This takes in the time domain of a signal into the frequency domain. Thus enabling you to look at exactly what frequencies exist. That is:

25. How do you remove missing or null values from a NumPy array?

To remove missing or null values from a NumPy array, you can use boolean indexing or filtering. Although NumPy does not natively support NaN handling like Pandas, you can still identify NaN values using np.isnan().

For example:

26. What is the difference between slicing and indexing in NumPy?

27. What are some of the limitations of NumPy?

• Memory Usage: NumPy arrays are less memory-efficient than specialised data structures for sparse data.
• Limited Functionality: This does not support features like handling categorical data or direct visualisation.
• Steep Learning Curve: Requires understanding of array broadcasting, dimensions, and indexing.
• Single Data Type: Arrays must-have elements of the same type, which limits flexibility compared to Python lists.
• No Built-in Null Support: Unlike Pandas, it lacks direct handling of NaN and None values for missing data.

Despite these limitations, NumPy remains a powerful library for numerical computing.

28. What is the use of diag() square matrix?

The np.diag() function extracts the diagonal elements of a square matrix or creates a diagonal matrix from a 1D array.

For example:

To create a diagonal matrix:

This function is particularly useful in linear algebra, where diagonal elements play a key role in operations like eigenvalue decomposition.

29. What is negative indexing in NumPy arrays?

Negative indexing is very useful when you don’t know the exact size of the array. All you have to do is assign an index to -1, which ultimately means the last index in the array, then -2 to the second last index in the array, and so on.

For example:

It also works with multidimensional arrays:

matrix = np.array([[1, 2], [3, 4]])

print(matrix[-1, -1])  # Output: 4

30.  What is np.sum() in NumPy and np.sum() in Python?

31.  Can you create a plot in NumPy?

NumPy itself does not have plotting functions. However, you can use NumPy in conjunction with Matplotlib, in order to create various different types of plots. For example, you can create data with the help of NumPy and then use Matplotlib to make interesting plots.

Example:

Here, NumPy generates the data, while Matplotlib handles the plotting. This combination is widely used in data visualisation tasks.

32.  How do you concatenate 2 NumPy arrays?

Example:

For multidimensional arrays:

33. How do you multiply 2 NumPy array matrices?

Matrix multiplication is performed using np.matmul() or the @ operator.

Example:

This calculates the dot product of rows and columns. For element-wise multiplication, use the * operator:

result = matrix1 * matrix2

print(result)

34.  What are the differences between structured arrays and record arrays in NumPy?

35.            How to reverse a NumPy array?

You can reverse an array using slicing with a step of -1.

Example:

For multidimensional arrays:

This approach reverses both rows and columns effectively.

NumPy Interview Questions and Answers for Experience Professionals

56.  Is SIMD used by NumPy?

Yes, NumPy implements SIMD (single instruction, multiple data) through custom low-level libraries such as BLAS and LAPACK. These libraries allow NumPy to perform the same operations on multiple data points simultaneously, specially optimised for calculating large numbers. By taking advantage of SIMD instructions, NumPy can handle vector addition. Even efficiently multiplies matrices and more. This parallel processing capability greatly accelerates array operations, making NumPy a powerful tool for scientific computing and data analysis. Modern CPUs with advanced SIMD features also help increase NumPy performance. This ensures that complex mathematical operations are carried out quickly.

57. How is vstack() different from hstack() in NumPy?

Example:

# Output: [1 2 3 4 5 6]

Here, for adding rows, you can use vstack() and when you need to add columns, you can use hstack().

58. How is fliplr different from flipud methods in NumPy?

fliplr and flipud are used to flip arrays horizontally or vertically.

Example:

# Output:

# Output:

Here, you can use fliplr for horizontal flips and flipud for vertical flips.

How will you implement the moving average for the 1D array in NumPy?

Implementing a moving average can be achieved using convolution.

Steps:

1. Define the window size.
2. Create a window with equal weights.
3. Use np.convolve() to compute the moving average.

Example:

# Output: [2. 3. 4. 5.]

This method efficiently calculates the average over a sliding window without explicit loops.

59. What happens when we use the array_split() method for splitting the NumPy array?

array_split() divides an array into multiple sub-arrays, handling uneven splits gracefully.

Example:

# Output: [array([1, 2, 3]), array([4, 5, 6]), array([7])]

Hare, array_split() will ensure if all elements are included or not, even if splits could be uneven.

60. What happens when the split() method is used for splitting NumPy arrays?

When you use split() method, it will divide any given array into equal parts. But you must give a divisible number, which can equally divide the array into that divisible number of splits only.

Example:

# Output: [array([1, 2]), array([3, 4]), array([5, 6])]

# Uneven split raises ValueError

np.split(arr, 4)  # Raises ValueError

Use split() when you need equal-sized sub-arrays and the array size allows it.

61. How is Vectorization Related to Broadcasting in NumPy?

Vectorisation and broadcasting are interconnected features that enhance NumPy’s efficiency.

Vectorisation:

• Definition: Performing operations on entire arrays without explicit loops.
• Benefit: Faster and more readable code.

Broadcasting:

• Definition: Automatically expanding arrays to compatible shapes for operations.
• Purpose: Enables operations on arrays of different sizes.

Relation: When you use vectorization, it generally relies on broadcasting, which is used to perform seamless element-wise operations, across varying array shapes.

Example:

# Output: [2 4 6]

Here, broadcasting allows the scalar b to be multiplied with each element of array a via vectorisation.

62.  How do you find the local peaks (or maxima) in a 1-D NumPy Array?

Identifying local maxima involves comparing each element with its neighbours.

Steps:

1. Compare each element (excluding the first and last) with its adjacent elements.
2. Use boolean indexing to identify peaks.

Example:

This method efficiently locates indices of local peaks within a 1D array.

63.  What do you understand by Vectorization in NumPy?

Vectorization in NumPy refers to executing operations on entire arrays without explicit loops, leveraging optimised low-level implementations.

Example:

# Output: [5 7 9]

Here, the addition is applied element-wise across the arrays without looping.

64. Write a program for interchanging two axes of the NumPy array.

Interchanging axes can be done using np.swapaxes() or np.transpose().

Using swapaxes():

Using transpose():

# Transpose axes

transposed = arr.transpose(2, 1, 0)

print(transposed.shape)  # Output: (4, 3, 2)

These functions effectively interchange the specified axes, altering the array’s structure for different computational needs.

65. Write a program for changing the dimension of a NumPy array.

Changing dimensions can be achieved using reshape() or np.newaxis.

Example Using reshape():

# Output:

Example Using np.newaxis:

# Output:

These methods adjust the array’s dimensions, facilitating various computational and data processing tasks.

66. Write a program for inserting space between characters of all elements in a NumPy array.

To insert spaces between characters, use NumPy’s string operations.

Example:

# Output:

Alternative Method:

spaced = np.array([‘ ‘.join(list(s)) for s in arr])

print(spaced)

Both approaches effectively add spaces between characters in each string element of the array.

67. Write a program for creating an integer array with values belonging to the range 10 and 60.

Use np.arange() or np.linspace() to create the desired range.

Using arange():

import numpy as np

arr = np.arange(10, 61)  # 10 to 60 inclusive

print(arr)

Using linspace() with dtype=int:

arr = np.linspace(10, 60, num=51, dtype=int)

print(arr)

Output:

[10 11 12 … 58 59 60]

These methods generate an integer array containing values from 10 to 60 efficiently.

68. What is the difference between NumPy’s arange() and linspace() functions?

arange() and linspace() both create sequences of numbers but differ in parameters and usage.

Key Differences:

• Step vs. Number of Points: arange() uses step size, while linspace() specifies the number of points.
• Inclusivity: linspace() includes the stop value by default (endpoint=True), whereas arange() excludes it.
• Floating Point Precision: linspace() is preferred for precise control over the number of elements.

69. Compare NumPy’s array() and asarray() functions.

array() and asarray() both create NumPy arrays but differ in copying behavior.

Example:

Choose based on whether a new copy is necessary or referencing is sufficient.

70. Explain the difference between NumPy’s flatten() and ravel() functions.

71. How do NumPy’s where() and nonzero() methods differ?

72. How is the broadcasting of arrays different from standard element-wise operations?

73. How does NumPy’s np.save() differ from np.savetxt()?