Here are 86 books that Python Data Science Handbook fans have personally recommended if you like
Python Data Science Handbook.
Shepherd is a community of 12,000+ authors and super readers sharing their favorite books with the world.
I am Wes McKinney, creator of the Python pandas project and author of Python for Data Analysis. I have been using Python for data work since 2007 and have worked extensively in the open source community to build accessible and fast data processing tools for Python programmers.
This is a super useful book published more recently that shows how to make the most of pandas’s deep toolbelt of features.
Compared with Python for Data Analysis, it explores some of the newer features added to pandas, and I think that any advanced pandas user will become more effective in their day to day work by reading it.
Best practices for manipulating data with Pandas. This book will arm you with years of knowledge and experience that are condensed into an easy to follow format. Rather than taking months reading blogs and websites and searching mailing lists and groups, this book will teach you how to write good Pandas code.
It covers:
Series manipulation
Creating columns
Summary statistics
Grouping, pivoting, and cross-tabulation
Time series data
Visualization
Chaining
Debugging code
and more...
The Victorian mansion, Evenmere, is the mechanism that runs the universe.
The lamps must be lit, or the stars die. The clocks must be wound, or Time ceases. The Balance between Order and Chaos must be preserved, or Existence crumbles.
Appointed the Steward of Evenmere, Carter Anderson must learn the…
I am Wes McKinney, creator of the Python pandas project and author of Python for Data Analysis. I have been using Python for data work since 2007 and have worked extensively in the open source community to build accessible and fast data processing tools for Python programmers.
This is a great follow-up book to Python Data Science Handbook.
Co-authored by one of the core developers of scikit-learn, this provides a deeper introduction to doing machine learning work in Python. This will give you a solid foundation to be able to move on later to deeper topics including deep learning or other AI topics.
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the…
I am Wes McKinney, creator of the Python pandas project and author of Python for Data Analysis. I have been using Python for data work since 2007 and have worked extensively in the open source community to build accessible and fast data processing tools for Python programmers.
Polars is a new modern data frame library, powered by Apache Arrow, that has grown like wildfire in the Python ecosystem in recent years. It provides many of the same capabilities as pandas, but with substantially better performance and scalability.
I recommend that Python programmers become proficient at both Polars and pandas, since while pandas is ubiquitous, more and more work will shift to Polars in the coming years.
Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing. With this hands-on guide, you'll walk through every aspect of Polars and learn how to tackle practical use cases using real-world datasets.
Jeroen Janssens and Thijs Nieuwdorp from Xomnia in Amsterdam show you how this superfast DataFrame library is perfect for efficient data wrangling, ETL pipelines, and so much more. This book helps you quickly learn the syntax and understand Polars' underlying concepts. You don't need to have experience with pandas or…
The Guardian of the Palace is the first novel in a modern fantasy series set in a New York City where magic is real—but hidden, suppressed, and dangerous when exposed.
When an ancient magic begins to leak into the world, a small group of unlikely allies is forced to act…
I am Wes McKinney, creator of the Python pandas project and author of Python for Data Analysis. I have been using Python for data work since 2007 and have worked extensively in the open source community to build accessible and fast data processing tools for Python programmers.
This is one of the best books I’ve read on how to write better code and build more maintainable software in Python. It is well-written, concise, and to the point.
Brett’s book is a perfect companion to the other books on this list, which are more focused on data analysis and using specific libraries to build data systems.
It's easy to start developing programs with Python, which is why the language is so popular. However, Python's unique strengths, charms, and expressiveness can be hard to grasp, and there are hidden pitfalls that can easily trip you up. This second edition of Effective Python will help you master a truly "Pythonic" approach to programming, harnessing Python's full power to write exceptionally robust and well-performing code. Using the concise, scenario-driven style pioneered in Scott Meyers' best-selling Effective C++, Brett Slatkin brings together 90 Python best practices, tips, and shortcuts, and explains them with realistic…
I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.
This could be the first stop of your brand new machine learning journey. I personally like how the technical concept is translated into plain English – each chapter starts with a high-level overview of a ML algorithm or methodology, concise and clear, followed by lots of visual examples and real world scenarios. I can guarantee you won’t get lost halfway. The book focuses on getting you introduced to ML with minimal math. But if you want to grasp some more of math, the next book I recommend is waiting for you.
NOTICE: To buy the newest edition of this book (2021), please search "Machine Learning Absolute Beginners Third Edition" on Amazon. The product page you are currently viewing is for the 2nd Edition (2017) of this book.
Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."
Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?
Well, hold on there...
Before you embark on your epic journey, there are some high-level theory and statistical principles to weave through first. But rather than spend…
I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.
Another practical book that I highly recommend. Its intuitive structure is the first thing I like about it. It gives you a comprehensive walkthrough of the ML workflow, from data exploration to learning. It covers abundant practical guides that get you prepared for real world challenges, such as how to handle outliers and to impute missing data. As a ML practitioner, I appreciate the dedicated case studies throughout the entire book. They really excite learners for future real world applications.
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application…
Aury and Scott travel to the Finger Lakes in New York’s wine country to get to the bottom of the mysterious happenings at the Songscape Winery. Disturbed furniture and curious noises are one thing, but when a customer winds up dead, it’s time to dig into the details and see…
I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.
This was my favorite book when I started my career. It talks about how information is processed, in an intelligent way, in the internet age. It acts as a tutorial to teach developers how to code our own ML programs, from online dating services, to document analyzer, and search engine. The author did an excellent job of explaining abstract ML algorithms with clear examples. His coding style in Python reads clearly, which makes the book more beginner-friendly.
Don’t get disappointed when you know this book is more than a decade old. It was a visionary book back in the day and it is still relevant today.
Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing,…
I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.
This book might as well be called Introduction to machine learning, and it is probably one of the only books truly deserving of the title. Did you know neural networks have been used for decades to scan checks at the bank? They are called Boltzman Machine. Have you ever heard of how decision trees were used in old-school data mining? You could only get them from proprietary software packages from the early 2000s.
In quant trading, you will constantly face compute power constraints, so it is invaluable to understand the mathematical foundations of the most old-school machine learning methods out there. Researchers 20 years ago used to do a lot of impressive work with a lot less computing power.
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
I’ve been a geeky kid all my life. (I don’t think I’ve quite grown up yet.) Born in the 1970s, my childhood was a wonderful playground of building robots and software. I was awarded one of the early degrees in AI, and a PhD in genetic algorithms. I’ve since spent 25 years exploring how to make computers think, build, invent, compose… and I’ve also spent 20 years writing popular science books. I’m lucky enough to be a Professor in one of the world’s best universities for Computer Science and Machine Learning: UCL, and I guess I’ve written two or three hundred scientific papers over the years. I still think I know nothing at all about real or artificial intelligence, but then does anyone?
OK, I’m biased here because Rob is an old friend of mine. We first met at academic conferences and had several heated debates (arguments). But after spending a little time together at a workshop we realised each probably knew what they were talking about after all. Robert Elliott Smith, I should make clear it's not the Rob Smith who writes about “Artificial Superintelligence”. Those books definitely do not make this list.
Our Rob is a coherent, grounded scientist with bags of real-world experience, and he brings his knowledge to this title with gusto, telling us about how AI is affecting our lives in ways you never thought possible – and often not in a good way. If you want to understand what can go wrong with AI and what we should be doing to stop it, don’t read about singularities or other such nonsense, read this.
We live in a world increasingly ruled by technology; we seem as governed by technology as we do by laws and regulations. Frighteningly often, the influence of technology in and on our lives goes completely unchallenged by citizens and governments. We comfort ourselves with the soothing refrain that technology has no morals and can display no prejudice, and it's only the users of technology who distort certain aspects of it.
But is this statement actually true? Dr Robert Smith thinks it is dangerously untrue in the modern era.
Magical realism meets the magic of Christmas in this mix of Jewish, New Testament, and Santa stories–all reenacted in an urban psychiatric hospital!
On locked ward 5C4, Josh, a patient with many similarities to Jesus, is hospitalized concurrently with Nick, a patient with many similarities to Santa. The two argue…
I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector. I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics.
Not everybody needs to be a data scientist, but everybody does need to be data literate. Without an intentional focus on evangelism and building a strong data culture in your organization it will be an uphill battle to make meaningful change. This book helps individuals and leaders to understand what data literacy is, and how we can build it like any other skill.
In the fast moving world of the fourth industrial revolution not everyone needs to be a data scientist but everyone should be data literate, with the ability to read, analyze and communicate with data.
It is not enough for a business to have the best data if those using it don't understand the right questions to ask or how to use the information generated to make decisions. Be Data Literate is the essential guide to developing the curiosity, creativity and critical thinking necessary to make anyone data literate, without retraining as a data scientist or statistician.
With learnings to show…
