1.
Introduction
2.
NumPY Interview Questions for Freshers
2.1.
1. What is NumPy?
2.2.
2. Why is NumPy favored over other programming languages and tools like IDL, Matlab, Octave, or Yorick?
2.3.
3. How do you count the number of times a particular value appears in an array of integers?
2.3.1.
Output:
2.4.
4. The input NumPy array is shown below. Column two should be removed and replaced with the new column listed below.
2.4.1.
Expected Output:
2.4.2.
Solution:
2.4.3.
Output:
2.5.
5. What Is The Distinction Between Numpy & Scipy?
2.6.
6. Is it possible to use eye() function to generate diagonal values?
2.7.
7. Is Python 3.x supported by Numpy and Scipy?
2.8.
8. Is it possible to use diag() to create a square matrix?
2.8.1.
Output:
2.9.
9. What are the benefits of NumPy Arrays over (nested) Python lists?
2.10.
10. Is SIMD used by NumPy?
2.11.
11. In NumPy, how do I change the data type of an array?
2.11.1.
Output:
2.12.
12. In NumPy, what is the difference between copy and view?
2.12.1.
Output:
2.12.2.
Output:
2.13.
13. Why not just have a Matrix Multiplication Operator?
3.
NumPY Interview Questions for Experienced
3.1.
14. What is the meaning of ndarray in NumPy, and How is it different from standard python sequence?
3.2.
15. Make a 3 * 3 matrix with values from 1 to 9.
3.2.1.
Output:
3.3.
16. In NumPy, what is the difference between ndarray and array?
3.4.
17. Is Numpy/scipy Compatible With Jython?
3.5.
18. Describe the concept of vectorization in NumPy.
3.6.
19. What Numpy/scipy tools are used to create plots?
3.7.
20. Print a range of four integers at random between 1-15.
3.7.1.
Output:
3.8.
21. How to reverse a NumPy array?
3.8.1.
Output:
3.9.
22. Is Numpy/scipy compatible with Ironpython(.net)?
3.10.
23. How can I make a 2D array?
3.10.1.
Output:
3.11.
24. How do I make a 3D or ND array?
3.11.1.
Output:
3.12.
25. Describe the operations that NumPy can execute.
4.
Conclusion
Last Updated: Mar 27, 2024
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# NumPy Interview Questions

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## Introduction

If you want to have a successful career in the technological field, you already know that a NumPy certification is one of the most in-demand credentials. After all, NumPy is based on de facto array computing standards.

So, here's a list of some often asked NumPy interview questions and answers that you might want to review before your next interview.

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## NumPY Interview Questions for Freshers

Here are some of the important Numpy Interview Questions for Freshers.

### 1. What is NumPy?

It is hands down one of the most asked numpy interview questions. NumPy, aka Numerical Python, is a free and open-source Python library. It's made to work with matrices and multidimensional arrays to conduct complicated mathematical, image processing, quantum computing, and statistical operations, among other things. Indexing, slicing, splitting, joining, reshaping, and other complicated manipulations are all possible with NumPy N-dimensional arrays.

NumPy is an excellent choice for data science applications because of its powerful built-in features. Because the demand for data scientists and machine learning professions is increasing, studying NumPy and carving a golden career path with it is a sensible choice.

### 2. Why is NumPy favored over other programming languages and tools like IDL, Matlab, Octave, or Yorick?

NumPy is a high-performance library for scientific calculations written in the Python programming language. Because it is open-source and free, it is chosen over IDL, Matlab, Octave, or Yorick. It also outperforms a generic programming language when it comes to linking Python's interpreter to C/C++ and Fortran code because it employs Python, which is a general-purpose programming language.

NumPy works with multi-dimensional arrays and matrices, allowing for complicated mathematical operations to be performed on them.

Check this out, Spring boot interview questions

### 3. How do you count the number of times a particular value appears in an array of integers?

The bincount() function can be used to count the number of times a given value appears. It's worth noting that the bincount() function can take either positive integers or boolean expressions as an argument. Negative integers aren't allowed to be utilized.

Use the NumPy.bincount function (). The resulting array is as follows:

``````import numpy as np
arr = np.array([0, 5, 4, 0, 4, 4, 3, 0, 0, 5, 2, 1, 1, 9])
print(np.bincount(arr))``````

### 4. The input NumPy array is shown below. Column two should be removed and replaced with the new column listed below.

``````import numpy as np
sa = np.array([[1,2,3],[4,5,6],[7,8,9]])
newColumn = NumPy.array([[0,0,0]])``````

#### Expected Output:

``````Printing Original Array:
[[1 2 3]
[4 5 6]
[7 8 9]]
Printing Array after deleting col 2 on axis 1
[[1 3]
[4 6]
[7 9]]
Printing Array after inserting col 2 on axis 1
[[1 0 3]
[4 0 6]
[7 0 9]]``````

#### Solution:

``````import numpy as np
print("Printing Original Array:")
sa = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(sa)
print("Printing Array after deleting col 2 on axis 1")
sa = np.delete(sa , 1, axis = 1)
print(sa)
arr = np.array([[0,0,0]])
print("Printing Array after inserting col 2 on axis 1")
sa = np.insert(sa , 1, arr, axis = 1)
print(sa)``````

#### Output:

Also read - Important Numpy Functions for ML

### 5. What Is The Distinction Between Numpy & Scipy?

NumPy would be perfect if it only had the array data type and the most basic operations: indexing, sorting, reshaping, basic elementwise functions, and so on. SciPy would house all numerical codes. However, compatibility is one of NumPy's main goals, therefore it tries to keep all features supported by any of its predecessors. As a result, NumPy includes several linear algebra routines that are better appropriately found in SciPy. In any case, SciPy has more advanced linear algebra modules, as well as a variety of additional numerical techniques. You should probably install both NumPy and SciPy if you're undertaking scientific computing with Python. The majority of new features belong in SciPy, not NumPy.

### 6. Is it possible to use eye() function to generate diagonal values?

``````import numpy as np
arr = np.eye(4)
print("\ndiaglonal values : \n",arr)``````

Output:

### 7. Is Python 3.x supported by Numpy and Scipy?

Python 2.x , as well as Python 3.2 and newer, are supported by NumPy and SciPy. NumPy 1.5.0 was the first version of NumPy to support Python 3. SciPy version 0.9.0 introduces Python 3 support.

### 8. Is it possible to use diag() to create a square matrix?

``````import numpy as np
arr = np.diag([1,2,3,4,5,6])
print("\n square matrix: \n",arr)``````

### 9. What are the benefits of NumPy Arrays over (nested) Python lists?

When compared to NumPy arrays, Python's lists, despite being extremely efficient containers capable of a wide range of functions, have many drawbacks. Vectorized operations, such as element-by-element addition and multiplication, are not conceivable.

Because they support objects of various types, they also require Python to retain the type information of each element. This means that every time an operation on an element is performed, a type dispatching code must be run. In addition, each iteration would have to go through type checks and require Python API accounting, resulting in C loops carrying very few actions.

### 10. Is SIMD used by NumPy?

NumPy has a flexible working mechanism that allows it to take advantage of CPU SIMD features to give quicker and more stable performance across all platforms.

### 11. In NumPy, how do I change the data type of an array?

``````import numpy as np
# Float to Integer
ar = np.array([3.3, 4.2, 5.1])
newar = ar.astype('i')
print(newar)
print(newar.dtype) ``````

### 12. In NumPy, what is the difference between copy and view?

Copy :

A replica of the original array is returned.

Do not use the original array's data or memory location.

Any changes made to the copy won't appear in the original.

``````import numpy as np
ar = np.array([1,2,3,4])
a= ar.copy()
a[0] = 5
print(ar)
print(a) ``````

#### Output:

View:

Returns a visual representation of the original array.

The data and memory location of the original array is used.

Any changes made to the copy will also be reflected in the original.

``````import numpy as np
arr = np.array([1,2,3,4])
a= arr.view()
#changing a value in the original array
arr[0] = 100
print(arr)
print(a) ``````

### 13. Why not just have a Matrix Multiplication Operator?

The @ symbol will be defined as a matrix multiplication operator in Python 3.5, and Numpy and Scipy will take advantage of it. PEP 465 was dedicated to this addition. To compensate for the unavailability of this operator in earlier versions of Python, separate matrix and array types were created.

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## 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.

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### 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)``````

### 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)``````

### 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)``````

### 25. Describe the operations that NumPy can execute.

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.

## Conclusion

In this article, we have extensively discussed THE MOST IMPORTANT Numpy Interview questions. We hope that the listed NumPy interview questions will assist you in preparing for your upcoming interviews.

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