Python NumPy Tutorial: Learn Python Numpy With Examples

Ever tried working with long lists of numbers in Python? It feels slow. What if there was a better, faster way? There is, and it’s called NumPy.

NumPy gives Python a superpower for handling numbers.  It adds a special kind of list, called an array, that's incredibly fast for math. This is why data science and machine learning in Python all start with NumPy.  

This guide is the perfect NumPy tutorial for anyone feeling lost. As a complete NumPy in Python tutorial, it will walk you through the absolute basics step-by-step. Think of it as your first, friendly NumPy tutorial to get you started on your data journey. Let's begin!

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What is Python NumPy?

NumPy stands for ‘Numerical Python.’ As you would’ve guessed, it focuses on numerical operations and computing. Numpy is a Python package and is the main library for scientific computations. It has n-dimensional array objects and tools to integrate other dominant languages such as C. You can use the NumPy array as an enormous multi-dimensional container for data. 

Advantages of selecting Numpy in Python tutorial

One advantage of a NumPy tutorial is that it empowers learners to efficiently process and analyze large datasets. Choosing NumPy in Python tutorial has the following benefits:

1. Fewer loops: NumPy aids in the reduction of loops and prevents you from becoming confused by iteration indices Without loops, your code will be more readable and resemble the equations you’re trying to solve.
2. Effective numeric computation: Compared to standard Python lists, NumPy operates on arrays more quickly and is optimised for numerical computations.
3. Better quality: NumPy is kept quick, user-friendly, and bug-free by hundreds of volunteers. NumPy employs C-coded algorithms that finish in nanoseconds as opposed to seconds.
4. Rapid Integration: SciPy, pandas, and Matplotlib are just a few of the well-known Python libraries that NumPy smoothly interacts with. Users may combine the benefits of many libraries for thorough data analysis processes thanks to this compatibility.
5. Broad Functionality: Numerous mathematical and statistical functions that are crucial for scientific computing and data analysis are provided by NumPy.

What is a NumPy Array?

The NumPy array is a fantastic n-dimensional array object. It has rows and columns, and you can use it to access the elements of a Python list. There are many operations you can perform on a NumPy array. We’ve discussed them later in the article, but before that, you must understand how to install NumPy in your system. Without installing it, you wouldn’t be able to use it. 

How to install NumPy?

You’ll have to go to the command prompt and enter ‘pip install numpy’ to install Python NumPy. After the installation is complete, you’ll have to go to the IDE and import numpy through ‘import numpy as np’. And that’s how you install Numpy on your system.

Ways to create arrays in NumPy

In the NumPy tutorial, arrays may be created in a variety of ways.

• The array method, for instance, allows you to turn a conventional Python list or tuple into an array. The type of the items in the sequences is used to determine the type of the resultant array.
• Frequently, an array’s items are initially unknown, but its size is known. As a result, NumPy provides a number of routines for building arrays using initial placeholder data. These reduce the need for costly array-growing operations. np.zeros, np.ones, np.full, np.empty, etc. are a few examples.
• NumPy has a method similar to the range for creating sequences of integers, except it outputs arrays rather than lists.

You can create arrays in NumPy easily through the following code:

import numpy as np
a=np.array([1,2,3])
print(a)

The output of the above code – [1 2 3]

The code above would give you a one-dimensional array. If you want to create a multidimensional array, you’d have to write something similar to the example present below:

a=np.array([(1,2,3),(4,5,6)])
print(a)

The output of the above code – [[ 1 2 3]

[4 5 6]]

Read more: Top 50 Python Project Ideas with Source Code in 2025 

Ways to do array indexing in NumPy

Understanding the fundamentals of array indexing is crucial for analysing and working with the array object. Numerous array indexing options are provided by NumPy.

• Slicing: NumPy arrays can be sliced in the same way as lists in Python can. Given that arrays might have many dimensions, you must define a slice for each dimension.
• Indexing an integer array: In this procedure, lists are supplied for each dimension’s indexing. To create a new arbitrary array, relevant items are mapped one to one.
• Indexing a Boolean array: When selecting elements from an array that meets a criterion, we utilise this approach.

What distinguishes a Python list from a NumPy array?

In a Python NumPy tutorial, it’s essential to understand the distinctions between Python lists and NumPy arrays. With NumPy, you have a wide range of tools for quickly and effectively creating arrays and manipulating data. A Python list can include a variety of data types, while NumPy arrays can only contain elements of the same data type. The proposed mathematical procedures would be exceedingly inefficient if the arrays were not uniform.

NumPy arrays offer vectorized operations and are optimised for numerical calculations. Using vectorized operations, it is possible to do element-wise calculations on whole arrays without using explicit loops. As a result, NumPy arrays perform numerical operations much quicker than Python lists, greatly increasing computational efficiency.

Learn NumPy from scratch in this free, full-length 2025 tutorial for Python beginners.

Operations in NumPy

Python NumPy has many operations. They all perform specific functions. Here are those functions with a brief description:

itemsize:

With the help of this function, you can find out the byte size of the elements of your array. Take a look at the following example:

import numpy as np
a = np.array([(1,2,3)])
print(a.itemsize)

The output of the above code – 4

ndim:

The ndim function helps you find the dimension of the array. You should know that you can have one dimensional, two dimensional, as well as three-dimensional arrays. Here’s an example of this function:

import numpy as np
a = np.array([(1,2,3),(4,5,6)])
print(a.ndim)

The output of the above code – 2

reshape:

With the help of the reshape operation, you can change the number of rows and columns present in an array. Suppose the one array has three columns and two rows. Through reshape, you can change them to 2 columns and three rows. See it in action through the following example:

import numpy as np

a = np.array([(8,9,10),(11,12,13)])
print(a)
a=a.reshape(3,2)
print(a)

Output of the above code – [[ 8 9 10] [11 12 13]] [[ 8 9] [10 11] [12 13]]

slicing:

By using the slicing operation, you can extract a specific set of elements from the required array. In other words, you can ‘slice’ the array and get a portion of the same. Suppose you have an array and want to extract a specific element from it, you’d go about it in the following way:

import numpy as np
a=np.array([(1,2,3,4),(3,4,5,6)])
print(a[0,2])

The output of the above code – 3

In the example above, the index of the first array was 0, and for the second one, it was 1. So, the code says that it should print the second element of the first array (that has the index 0). Suppose you need the second element from the first and the zeroth index of the array. Then we would use the following code:

import numpy as np
a=np.array([(1,2,3,4),(3,4,5,6)])
print(a[0:,2])

The output of the above code– [3 5]

Also read: Python Developer Salary in India in 2025

dtype:

WIth the dtype function, you have the option of finding the data type of the elements of an array. It gives you the data type and the size of the required component. Take a look at the following example to see how it works:

import numpy as np
a = np.array([(1,2,3)])
print(a.dtype)

The output of the above code – int32

You can use the ‘shape’ and ‘size’ functions to find the shape and size of the array as well. Take a look at this example of our Python NumPy tutorial to understand these functions properly:

import numpy as np
a = np.array([(1,2,3,4,5,6)])
print(a.size)
print(a.shape)

The output of the above code – 6 (1,6)

linspace:

With the help of the linspace operation, you can get evenly spaced numbers spread according to your mentioned interval. The linspace function has its uses, and here’s an example of how you can use it:

import numpy as np
a=np.linspace(1,3,10)
print(a)

Output of the above code– [ 1. 1.22222222 1.44444444 1.66666667 1.88888889 2.11111111 2.33333333 2.55555556 2.77777778 3. ]

square root and standard deviation

Python NumPy enables you to perform various mathematical operations. And one of those operations is deriving the square root of the required array. You can also obtain the standard deviation of your NumPy array. Here’s a detailed example to help you in this regard:

import numpy as np
a=np.array([(1,2,3),(3,4,5,)])
print(np.sqrt(a))
print(np.std(a))

The output of the above code– [[ 1. 1.41421356 1.73205081]

[ 1.73205081 2. 2.23606798]]

1.29099444874

max/min

You can find the maximum, minimum, and the sum of an array as well through the specific operations. Finding the maximum and the minimum can help you a lot in performing complex operations. Here is how you can find the maximum, minimum, and the sum of the array you have:

import numpy as np
a= np.array([1,2,3])
print(a.min())
print(a.max())
print(a.sum())

The output of the above code – 1 3 6

Horizontal and vertical stacking

You might want to combine two arrays but not add them, i.e., you might just want to concatenate them. For that purpose, you can either stack them vertically or horizontally. Here is the example code for doing so:

import numpy as np
x= np.array([(1,2,3),(3,4,5)])
y= np.array([(1,2,3),(3,4,5)])
print(np.vstack((x,y)))
print(np.hstack((x,y)))

Output of the above code – [[1 2 3] [3 4 5] [1 2 3] [3 4 5]]

[[1 2 3 1 2 3] [3 4 5 3 4 5]]

Read more: Operators in Python: A Beginner’s Guide to Arithmetic, Relational, Logical & More 

Addition

You can add NumPy arrays as well. Apart from addition, you can also perform subtraction, division, and multiplication of two matrices. Here’s an example of addition in Python NumPy:

import numpy as np
x= np.array([(1,2,3),(3,4,5)])
y= np.array([(1,2,3),(3,4,5)])
print(x+y)

The output of the above code – [[ 2 4 6] [ 6 8 10]]

Like we mentioned earlier, you can perform other mathematical operations on NumPy arrays as well, including subtraction and division. Here’s how:

import numpy as np
x= np.array([(1,2,3),(3,4,5)])
y= np.array([(1,2,3),(3,4,5)])
print(x-y)
print(x*y)
print(x/y)

Output of the above code– [[0 0 0] [0 0 0]]

[[ 1 4 9] [ 9 16 25]]

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

ravel

The ravel operation lets you convert a NumPy array into a ravel, which is a single column. Here’s an example:

import numpy as np
x= np.array([(1,2,3),(3,4,5)])
print(x.ravel())

The output of the code – [ 1 2 3 3 4 5]

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Conclusion

Mastering NumPy is a game-changer, and you're now well on your way. This NumPy tutorial has walked you through the essentials, from arrays to core operations, unlocking the library that powers all of data science.

The key is to keep applying what you've learned. This NumPy tutorial will always be here as a reference, so challenge yourself with new problems and put your powerful new skills to good use!

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