The Python framework is often used for data analysis and manipulation. But, Python does not out-of-the-box offer any advanced tools for these tasks. Lists in Python are very complex data structures, and they take a major hit in performance when used for large datasets. This is where NumPy, an array operations library, can be useful.
This article takes a look at NumPy, its important features, its use, and the resources available to learn it.
How to Learn NumPy
As NumPy is a complex and powerful library to manipulate scientific data, so it is important to understand its purpose and features before we dive into learning its dynamics.
What is NumPy?
NumPy is a powerful Python library used for scientific computations and array operations. It stands for Numerical Python and contains a multi-dimensional array and matrix data structures. NumPy is an extension of Numeric and Numarray, and is one of the most important libraries to know if you’re looking to make a career in data analysis using Python.
NumPy is a wrapper written around a library originally implemented in the C language. This makes operations on NumPy objects super fast. NumPy arrays are well-known to beat Python Lists in performance. Other entities like Pandas objects rely heavily on NumPy objects, as the Pandas library is an extension of the NumPy library.
Features of NumPy
NumPy offers multiple features as a data visualization tool. Here are some of them:
High-Performance Arrays
Arrays are the most important feature of the NumPy library. NumPy arrays are fast, as they are simple C arrays wrapped around with Python APIs. This provides a huge performance boost to the NumPy arrays over Python List. This is because Python Lists use a complex structure of referring to the actual memory locations, while C arrays use a linear list of references for easy traversal. These arrays can be one or multi-dimensional as well. This means you can create a matrix of any shape (from 1×1 to NxN) using NumPy.
Supports Interlanguage Execution
NumPy has functions that can help work with code written in other languages. This allows users to integrate code written across a variety of programming languages. For instance, you can use the NumPy C-API to run code originally written in C. This provides a great deal of flexibility, as you can easily utilize the speed and optimization of C for complex application requirements.
Wide Array of Scientific and Mathematical Operations
NumPy offers a variety of complex operations, including but not limited to linear algebra, and Fourier transform. There are separate modules for each of these operations, making NumPy a diverse library for working with arrays.
Supports Varied Data Types
The NumPy library supports the handling data of different types like Boolean, strings, and integers. The dtype function is used to determine the data type of a NumPy object. While creating arrays, dtype can be passed in as an argument to perform data type-specific operations on the array.
What is NumPy Used for?
NumPy is a popular choice for carrying out scientific data operations in Python. Before we set down to learn the library, let’s explore some of its use cases:
Manipulating Arrays
NumPy is the go-to for data handling tasks that require fast and reliable arrays of data. NumPy arrays are extremely fast, so major libraries like Pandas and SciPy rely on them for storing large datasets.
Mathematical Operations
The second-best use of NumPy is to carry out mathematical operations. While the NumPy library houses very powerful arrays, it also contains highly optimized functions to carry out the most common mathematical operations on this data.
Supporting Other Mathematical Libraries
Prominent data analysis libraries like Pandas, Matplotlib, and SciPy rely on NumPy objects for faster data processing. Operating on NumPy arrays is much more fast and efficient when compared to operating on Python Lists.
Learning NumPy
Given that NumPy is such a robust scientific computation library in Python, it is important to know how to use it to the fullest if you are planning to work with data analysis in the future. The following is a list of resources to help you get started.
The Best NumPy Resources
As NumPy is an open-source library, a lot of people have tried to create content to help others learn to use the library. Let’s first take a look at the free and paid video courses that are available for NumPy:
Python NumPy Tutorial for Beginners by freeCodeCamp.org
- Platform: YouTube
- Duration: About one hour
- Price: Free
- Prerequisites: Basic understanding of mathematical operations
- Start Date: On-demand
FreeCodeCamp offers the best beginner-friendly resources on most of the technical topics for free. This course is a perfect starter for somebody who understands how mathematical operations of matrices work and is looking to get started with the NumPy library. The first half of the course focuses on explaining how arrays can be implemented better with NumPy, and the second half focuses on complex operations that can be operated on arrays using NumPy.
Python NumPy Full Course in 2020 by Rylan Fowers
- Platform: YouTube
- Duration: About one hour
- Price: Free
- Prerequisites: Basic understanding of mathematical operations
- Start Date: On-demand
Available as a single-video crash course on YouTube, this course is one of the best independent courses on NumPy. It begins with the very basics and covers each topic in great detail. The description contains a handy index of all topics with their timestamps, so it is a great resource for the second-timers as well. It groups topics based on use-cases, and covers everything from creating, combining to exporting NumPy objects.
Deep Learning Prerequisites: The Numpy Stack in Python (V2+)
- Platform: Udemy
- Duration: Six hours
- Price: About $10
- Prerequisites: Basic understanding of mathematical operations
- Start Date: On-demand
Rated 4.6-stars by over 18,000 learners, this course is the perfect way to begin your journey with NumPy. It serves as the perfect preparation for any data analysis-based task that you may take later like Deep Learning, and Machine Learning. Apart from a basic theoretical explanation, the tutor demonstrates how NumPy and other related libraries work with code.
No knowledge of advanced mathematics is necessary, but a quick refresher on basic topics like the dot product and matrix inversion is great before beginning with this course.
NumPy Books
Apart from video courses, many books are available to help you get started with NumPy. Some top ones include:
‘Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython’ by William McKinney
Priced about $20 on Amazon, this is one of the best books for taking your first step in the world of NumPy and data analysis in Python. The book is written by the creator of the Python Pandas project, making this a complete resource for learning NumPy for data processing tasks in Python. Apart from NumPy, this book focuses on other things like the Pandas library and the IPython environment. This makes it a complete resource for getting started in the world of data analytics with Python.
‘NumPy Beginner’s Guide – Second Edition’ by Ivan Idris
Priced at about $30 at the moment (check the latest price here), this book is one of the best options for people who are looking to learn NumPy from scratch. It focuses only on NumPy, so it can help you develop strong foundational knowledge. It is a beginner-friendly book and requires no prerequisites for getting started.
NumPy Cookbook by Ivan Idris
Retailing at about $30 or $10 on Kindle, this book is a great asset for those who have had some experience with the library and are looking for some advanced learning. The book is the perfect alternative if you have a basic understanding of how NumPy works. It is written in the cookbook format and involves practical recipes to help you get going.
NumPy Resources
Apart from the video courses and books, there are plenty of tutorials available online. Here are just some of the great pieces:
Learn by NumPy
Offered by the NumPy organization itself, Learn offers a series of beginner-friendly video tutorials to help you get started with the library. This tutorial is made for all users and classifies the content according to difficulty. It’s a good alternative to begin with, but you are going to need to take at least one more tutorial to help make your foundations stronger.
Python NumPy Tutorial: Learn with Code Examples by Guru99.com
With ample content on introduction, installation and advanced operation, this course offers the perfect roadmap to mastering NumPy. It is a great place to begin with, as it offers helpful subtext throughout the tutorial. However, it does not seem practical enough to make you an expert with the given content. If you’re looking for some advanced content, couple this with a more advanced course.
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NumPy Tutorial by TutorialsPoint.com
This resource is like a glossary of subtopics of NumPy. However, the depth of content on each subtopic is great, and this can be an important asset to you once you are done with the basics of the library. This resource is great for people who have had some experience with the topic, and are looking for a good reference point for the future.
Linear Regression with NumPy and Python on Coursera
This one is a 1.5 hours long, free guided-project offered by Coursera. It can help you implement NumPy code along with an experienced instructor. This project does not aim at teaching you the basics of Python scientific computation, rather it guides you along on how to implement NumPy in your Python code. It is important to have a prior theoretical understanding of the subject before beginning with this project.
How Long Does It Take to Learn NumPy?
Given that NumPy is a powerful and detailed library, it usually takes about three weeks to get a good start. Rigorous practice, aided with sample projects for a duration of another one to two weeks is adequate to make the basics pretty clear in your mind. If you are looking to become an expert in this topic, you will need a minimum of six to eight weeks, with regular learning and application.
Should You Study NumPy?
After compiling a great list of courses and content on NumPy, we are now faced with the most important question of all: should you learn NumPy?
The answer to this is simple: if you are willing to dive into the world of data analysis, or if your day to day job involves sifting through huge datasets and machine learning models, NumPy is the best skill to invest your time and resources into. It offers great performance and varied operations, making it the perfect alternative for quick data storage and access.
If you are looking to make a career as a data analyst in Python-based roles and projects, NumPy is one of the basic elements to be able to meet your job requirements. There are some additional libraries that you may need to know like Pandas and SciPy, but a solid foundation in NumPy is essential before beginning with them.
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