NumPY Interview Questions

NumPY Interview Questions for Experienced

14. What is the meaning of ndarray in NumPy, and How is it different from standard python sequence?

The ndarray object lies at the heart of the NumPy package. Many operations are performed in compiled code for performance, and this encapsulates n-dimensional arrays of homogeneous data types. Between NumPy arrays and normal Python sequences, there are a few key differences:

Unlike Python lists, NumPy arrays have a fixed size when created (which can grow dynamically). When the size of a ndarray is changed, a new array is created and the old one is deleted.

In a NumPy array, all of the elements must be of the same data type. The exception is that arrays of (Python, including NumPy) objects can be used, allowing for arrays of various sizes.

NumPy arrays make it easier to do advanced mathematical and other operations on massive amounts of data. Such actions are typically performed more quickly and with less code than utilizing Python's built-in sequences.

NumPy arrays are used by a growing number of scientific and mathematical Python-based packages; while they normally support Python-sequence input, they convert it to NumPy arrays before processing, and they frequently output NumPy arrays. To put it another way, knowing how to utilize Python's built-in sequence types isn't enough to efficiently use much (if not all) of today's scientific/mathematical Python-based software; you also need to know how to use NumPy arrays.

Also read, accounts payable interview questions
 

15. Make a 3 * 3 matrix with values from 1 to 9.

import numpy as np
arr = np.arange(1,10).reshape(3,3)
print(arr)

 

Output: 

 

Also Read - NumPy.Reshape() in Python

16. In NumPy, what is the difference between ndarray and array?

Numpy.array is only a helper function for making a ndarray; it isn't a class in and of itself. You can alternatively use numpy. ndarray to create an array; however, this is not advised.

You can generate an array from the ndarray class in two ways, as shown below:

Using the methods array(), zeros(), and empty(): Arrays should be built with array, zeros, or empty values. The parameters relate to a low-level mechanism for instantiating an array (ndarray(...)).

Directly from the ndarray class: When using __new__ to create an array, there are two options: Only shape, dtype, and order are used in the buffer is None. All keywords are interpreted if the buffer is an object that exposes the buffer interface.
 

17. Is Numpy/scipy Compatible With Jython?

No. Simply explained, Jython runs on top of the Java Virtual Machine and has no way of interacting with C extensions for the regular Python interpreter (CPython).
 

18. Describe the concept of vectorization in NumPy.

Vectorization applies a single elemental operation to all of the elements in an array. The implementations below are written in C, resulting in significant speed improvements. NumPy already vectorizes a huge number of operations, such as all arithmetic operators, logical operators, and so on. Numpy also gives you the option of vectorizing your function. All you have to do now is:

Create a function that performs the desired operation and takes the members of the array as arguments.

The function should be vectorized.

This vectorized function requires the arrays as inputs.
 

19. What Numpy/scipy tools are used to create plots?

NumPy and SciPy, which are focused on numerical objects and methods, do not provide plotting. Several programs, like the widely used Matplotlib and the versatile, modular toolkit Chaco, work closely with NumPy to produce high-quality charts.
 

20. Print a range of four integers at random between 1-15.

import numpy as np
rand = np.random.randint(1,20,4)
print("\n Random number ranges from 1-20 ",rand)

 

Output:

 

Also read - Vectorization in NumPy 

21. How to reverse a NumPy array?

import numpy as np
arr = np.array([1,2,3,4,5,6])
reversed_arr = arr[::-1]
print(reversed_arr)

 

Output: 

 

22. Is Numpy/scipy compatible with Ironpython(.net)?

On 32-bit Windows, some users have reported success with NumPy and Ironclad. The present state of Ironclad support for SciPy is uncertain, but it is less possible than NumPy due to various complicating circumstances (most notably the Fortran compiler situation on Windows).
 

23. How can I make a 2D array?

import numpy as np
num2=[[3,2,1],[4,5,6]]
num2 = np.array(num2)
print("\n2d array : \n",num2)

 

Output:

 

24. How do I make a 3D or ND array?

import numpy as np
num=[[[3,2,1],[6,5,4],[9,8,7]]]
num = np.array(num)
print("\n3d array : \n",num)

 

Output:

 

25. Describe the operations that NumPy can execute.

Add: '+' represents the addition of elements from two arrays.

Multiply: Multiplies the items by a certain number, as indicated by the asterisk (*).

Power: Array items can be squared using the '**' operator.

Utilize the syntax below to use a higher power-like cube.

Conditional Expressions: A conditional expression compares the items of an array.

Must Read How to install numpy in python

Here the Java Numpy interview questions have come to an end. The areas covered in this Numpy interview questions are the most sought-after skill sets in a Numpy Professional, according to the recruiters. These Numpy interview questions will undoubtedly assist you in acing your next employment interview. 

NumPy Coding Questions

26. Write a NumPy code to create a 3x3 matrix with values ranging from 1 to 9.

Code:

 

Output:

[[1 2 3]
 [4 5 6]
 [7 8 9]]

27. How do you compute the mean of a NumPy array?

Code:

 

Output:

3.0

28. Write a NumPy function to get the maximum value from each column of a 2D array.

Code:

 

Output:

[3 6 9]

29. How do you flatten a 2D NumPy array into a 1D array?

Code:

 

Output:

[1 2 3 4]

30. Write a NumPy code to find the indices of non-zero elements in a NumPy array.

Code:

 

Output:

(array([1, 3, 4]),)

31. How do you calculate the dot product of two NumPy arrays?

Code:

 

Output:

32

32. Write a NumPy code to generate a 5x5 matrix with random values.

Code:

 

Output:

([[0.73412622 0.88546026 0.17684374 0.51251759 0.57160111]
  [0.48517744 0.46934632 0.8633715  0.79571554 0.47058272]
  [0.86794944 0.23768931 0.33422847 0.20485051 0.28566529]
  [0.02016532 0.02406553 0.43684438 0.04819661 0.8938922 ]
  [0.3345864  0.24238151 0.32977553 0.55879966 0.52927757]])

33. How do you find the unique elements in a NumPy array?

Code:

 

Output:

[1 2 3 4 5]

34. Write a NumPy code to compute the standard deviation of a given array.

Code:

 

Output:

1.4142135623730951

35. How do you create an identity matrix using NumPy?

Code:

 

Output:

[[1. 0. 0. 0.]
 [0. 1. 0. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]]

NumPy MCQ Questions

1. Which function is used to create an array of zeros in NumPy?

A. np.ones()

B. np.zeros()

C. np.empty()

D. np.full()

Answer: B. np.zeros()

2. What does the reshape() method do in NumPy?

A. Changes the data type of the array

B. Reshapes the array without changing its data

C. Flattens the array into one dimension

D. Adds a new dimension to the array

Answer: B. Reshapes the array without changing its data

3. How can you generate a NumPy array with evenly spaced values between 0 and 1?

A. np.linspace(0, 1, num)

B. np.arange(0, 1, step)

C. np.random.rand()

D. np.ones()

Answer: A. np.linspace(0, 1, num)

4. Which method is used to compute the sum of all elements in a NumPy array?

A. np.total()

B. np.add()

C. np.sum()

D. np.count()

Answer: C. np.sum()

5. What is the purpose of the np.concatenate() function?

A. To join arrays along an existing axis

B. To split an array into sub-arrays

C. To sort an array

D. To duplicate an array

Answer: A. To join arrays along an existing axis

6. How do you access the third element of a NumPy array?

A. array[3]

B. array[2]

C. array.get(3)

D. array.fetch(2)

Answer: B. array[2]

7. What does np.argmax() return?

A. The index of the maximum value

B. The maximum value

C. The position of the minimum value

D. The number of maximum values

Answer: A. The index of the maximum value

8. How can you transpose a NumPy array?

A. np.transpose(array)

B. np.flip(array)

C. array.T

D. array.transpose()

Answer: C. array.T

9. Which function is used to create an array with random integers?

A. np.random.randint()

B. np.random.random()

C. np.random.randn()

D. np.random.range()

Answer: A. np.random.randint()

10. What does np.dot() compute?

A. The element-wise multiplication of arrays

B. The dot product of two arrays

C. The sum of two arrays

D. The cross product of two arrays

Answer: B. The dot product of two arrays

Conclusion

In this article, we have discussed NumPy Interview Questions. NumPy is a fundamental library for numerical computing in Python, providing powerful tools for handling and analyzing large datasets with efficiency. This blog covered essential and advanced NumPy concepts, including coding challenges and multiple-choice questions, designed to enhance your understanding and prepare you for interviews.

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