What if the key to mastering Python was hidden in just the right books? Python’s popularity has surged, powering everything from web development to artificial intelligence. Yet, with so many resources out there, choosing the right book can feel overwhelming. Why does Python continue to dominate programming conversations worldwide? Because it’s versatile and accessible, but only if you know where to start.
Experts like Hilary Mason, a data scientist known for her work at Accel, and Kirk Borne, Principal Data Scientist at Booz Allen Hamilton, have spent years navigating Python’s vast landscape. Hilary praises "Automate the Boring Stuff with Python" for turning tedious tasks into programming victories, while Kirk champions "Python Machine Learning" for bridging theory and practical data science. Their endorsements are grounded not just in expertise but in real-world application.
These curated selections cover everything from beginner-friendly introductions to deep dives into machine learning and security. While these expert-curated books provide proven frameworks, readers seeking content tailored to their specific experience level, profession, or goals might consider creating a personalized Python book that builds on these insights and fits your unique journey.
Charlie Miller, a security researcher known for his hacking expertise, praises this book as a fun read from seasoned professionals willing to share their hard-earned secrets. After years navigating cybersecurity challenges, he finds it a great way to get started on the right path in Python hacking. His endorsement highlights the practical value this book adds, even if it doesn't instantly create elite hackers. Similarly, Stephen Northcutt, founding president of the SANS Technology Institute, notes the book’s lasting relevance, remarking that many programs here could remain useful for a decade with minor tweaks, a rarity in security literature.
“Black Hat Python is a fun read written by experts with years of experience who are willing to share the secrets they have learned along the way. While it might not immediately turn you into a super stunt hacker like me, it will certainly get you started down the correct path.”
Justin Seitz is an independent security consultant who has trained and consulted with Fortune 500 companies, law enforcement agencies, and governments around the world. He is the creator of Hunchly and the author of Gray Hat Python, the first book to cover Python for security analysis. Justin can be found teaching OSINT techniques on his blog and is active on Twitter as @jms_dot_py.
When Justin Seitz discovered how Python could be wielded for offensive security, he crafted this guide to empower hackers and penetration testers with real coding skills. You’ll learn to build tools for network sniffing, credential theft, privilege escalation, and stealthy trojans, all updated for Python 3.x. The book doesn’t just skim the surface; it dives into advanced topics like memory forensics with the Volatility Framework and automation through Windows COM. If you're aiming to deepen your hacking toolkit with practical Python applications, this book offers targeted skills, though it assumes some prior programming familiarity.
Hilary Mason, co-founder of Hidden Door Co. and Data Scientist in Residence at Accel, recommends this book for its unique way of framing programming as small but satisfying triumphs of automation. After navigating complex data science challenges, she found that this book makes the process of programming approachable and even fun, emphasizing practical victories over theoretical depth. As she puts it, "The best part of programming is the triumph of seeing the machine do something useful. Automate the Boring Stuff with Python frames all of programming as these small triumphs; it makes the boring fun." This perspective helped shift her thinking towards appreciating automation's immediate benefits. Additionally, Mashable highlights its value for office workers and academics by detailing how to automate tasks like Excel programming and web crawling, making it a versatile tool for productivity enhancement.
Co-Founder Hidden Door Co., Data Scientist in Residence at Accel
“The best part of programming is the triumph of seeing the machine do something useful. Automate the Boring Stuff with Python frames all of programming as these small triumphs; it makes the boring fun.”
Al Sweigart is a professional software developer who teaches programming to kids and adults. Sweigart has written several bestselling programming books for beginners, including Automate the Boring Stuff with Python, Invent Your Own Computer Games with Python, Cracking Codes with Python, and Coding with Minecraft (all from No Starch Press).
Unlike most Python books that dive straight into complex programming concepts, Al Sweigart’s approach here is to show you how to make your computer do tedious tasks automatically, even if you’re new to coding. You’ll learn practical skills like automating file renaming, web scraping, filling forms, and manipulating spreadsheets through clear examples and updated projects, including a new chapter on input validation and Gmail automation. This hands-on style means you’re not just reading theory but actually building tools that save you time. If you want to start programming with immediate, tangible results — especially to boost productivity — this book suits you well, though experienced developers looking for deep Python internals might want to look elsewhere.
This AI-tailored book on Python projects develops a systematic approach with frameworks that adapt to your specific coding experience and goals. The content adjusts based on your interests and background to address the practical challenges of building real-world Python applications. Created after you specify your focus areas, it bridges theoretical knowledge and hands-on implementation, fostering skill growth through tailored project work.
TailoredRead AI creates personalized nonfiction books that adapt to your unique background, goals, and interests. Instead of reading generic content, you get a custom book written specifically for your profession, experience level, and learning objectives. Whether you're a beginner looking for fundamentals or an expert seeking advanced insights, TailoredRead crafts a book that speaks directly to you. Learn more.
2025·50-300 pages·Python, Python Fundamentals, Project Planning, Code Organization, Data Handling
This personalized book provides a tailored framework for building practical Python projects that enhance coding skills through hands-on experience. It focuses on creating project-based learning paths that adjust to your proficiency level, industry, and goals, enabling you to apply Python concepts to real-world scenarios effectively. The book covers methodologies for selecting, designing, and implementing projects that reinforce programming fundamentals and advanced techniques alike. By cutting through generic advice, it fits your specific context to promote skill development through practical application. The tailored approach ensures that each project contributes directly to your coding competency and problem-solving abilities in Python.
Mike Bayer, creator of SQLAlchemy, is a respected voice in the Python community, and his endorsement carries weight, especially for anyone serious about mastering Python. He highlights how this book caught him up on Python 3's latest features—from f-strings to the magic method init_subclass()—and offered best practices he plans to quote in his work. This recommendation comes from his deep experience managing complex Python codebases, making it a trusted guide for developers navigating the transition from Python 2. Joining him, Wes McKinney, creator of the Python Pandas project, praises this edition as a rich source of wisdom for programmers at any level, underscoring its broad appeal and practical value.
“Now that Python 3 has finally become the standard version of Python, it’s already gone through eight minor releases and a lot of new features have been added throughout. Brett Slatkin returns with a second edition of Effective Python with a huge new list of Python idioms and straightforward recommendations, catching up with everything that’s introduced in version 3 all the way through 3.8 that we’ll all want to use as we finally leave Python 2 behind. Early sections lay out an enormous list of tips regarding new Python 3 syntaxes and concepts like string and byte objects, f-strings, assignment expressions (and their special nickname you might not know), and catch-all unpacking of tuples. Later sections take on bigger subjects, all of which are packed with things I either didn’t know or which I’m always trying to teach to others, including ‘Metaclasses and Attributes’ (good advice includes ‘Prefer Class Decorators over Metaclasses’ and also introduces a new magic method ‘__init_subclass__()’ I wasn’t familiar with), ‘Concurrency’ (favorite advice: ‘Use Threads for Blocking I/O, but not Parallelism,’ but it also covers asyncio and coroutines correctly) and ‘Robustness and Performance’ (advice given: ‘Profile before Optimizing’). It’s a joy to go through each section as everything I read is terrific best practice information smartly stated, and I’m considering quoting from this book in the future as it has such great advice all throughout. This is the definite winner for the ‘if you only read one Python book this year...’ contest.”
Brett Slatkin is a principal software engineer at Google. He is the technical co-founder of Google Surveys, the co-creator of the PubSubHubbub protocol, and he launched Google’s first cloud computing product (App Engine). Fourteen years ago, he cut his teeth using Python to manage Google’s enormous fleet of servers. Outside of his day job, he likes to play piano and surf (both poorly). He also enjoys writing about programming-related topics on his personal website (https://onebigfluke.com). He earned his B.S. in computer engineering from Columbia University in the City of New York. He lives in San Francisco.
When Brett Slatkin first drew from his extensive experience managing Google's server infrastructure, he crafted a book that goes beyond basic Python tutorials to reveal the subtleties of writing truly Pythonic code. You’ll find 90 distinct insights covering everything from comprehensions and generator functions to concurrency and metaclasses, explained with clear examples like the new magic method __init_subclass__(). This book suits programmers who want to deepen their mastery of Python 3’s evolving features and write code that’s not only functional but also elegant and maintainable. If you’re comfortable with Python basics and ready to refine your approach, this book provides the nuanced understanding you need.
Pratham Prasoon, a self-taught programmer contributing to modular blockchain projects, found himself relying heavily on this book during a demanding research internship. He highlights how the book's clear, concise explanations of both deep and classical machine learning theories made complex topics approachable and practical. "This book was a lifesaver during my research internship! You'll learn about deep and classical machine learning with great to-the-point theory explanations," he says, recommending it for those with some Python experience. Following his lead, Santiago, a seasoned machine learning writer, praises the book's substantial content, noting its extensive 530 pages packed with valuable insights.
Self-taught programmer at CelestiaOrg, ML enthusiast
“Last but not least, we have Machine Learning with PyTorch and Scikit-Learn. This book was a lifesaver during my research internship! You'll learn about deep and classical machine learning with great to-the-point theory explanations. Suitable for slightly more advanced readers.” (from X)
Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili(you?)·
About the Author
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence. Yuxi (Hayden) Liu is a Software Engineer, Machine Learning at Google. He is developing and improving machine learning models and systems for ads optimization on the largest search engine in the world. Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
When Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili developed this guide, they aimed to demystify machine learning and deep learning using Python's PyTorch and scikit-learn libraries. You’ll gain a strong foundation in essential algorithms, from classifiers to neural networks, with detailed chapters on data preprocessing, ensemble learning, and even cutting-edge topics like graph neural networks and transformers. The book balances theory and practice, showing you not just how to run code but why models behave as they do—perfect if you already know Python and want to deepen your machine learning skills. If you seek a resource that bridges classical methods with modern deep learning frameworks, this book fits that niche well.
Kirk Borne, Principal Data Scientist at Booz Allen Hamilton and an astrophysicist, highlights his admiration for this book as a go-to Python resource when learning machine learning quickly, tweeting, "Tips & Tutorials on How to Learn Machine Learning in 10 Days by Sebastian Raschka — Must see his comprehensive Python coding book." His endorsement stems from witnessing the book's clarity in explaining complex concepts through accessible language and real-world examples. This perspective is echoed by Sebastian Thrun, CEO of Kitty Hawk and co-founder of Udacity, who calls it "a highly practical, hands-on book covering machine learning from theory to practice," recommending it to those aspiring to become experts. Their combined insights underscore this book’s balanced approach between foundational theory and practical implementation, making it a reliable companion for developers diving into machine learning with Python.
Principal Data Scientist, Executive Advisor at Booz Allen Hamilton
“A brilliantly approachable introduction to machine learning with Python. Raschka and Mirjalili break difficult concepts down into language the layperson can easily understand while placing these examples within real-world contexts. A worthy addition to your machine learning library!”
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology. Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University. He recently joined 3M Company as a research scientist, where he uses his expertise and applies state-of-the-art machine learning and deep learning techniques to solve real-world problems in various applications to make life better.
Unlike most Python books that focus narrowly on syntax or basic programming, Sebastian Raschka and Vahid Mirjalili dive deeply into machine learning and deep learning frameworks with clarity and precision. Drawing from Raschka's academic research and Mirjalili's applied expertise, the book guides you through using scikit-learn and TensorFlow 2 to build models from scratch, including chapters on GANs, reinforcement learning, and sentiment analysis. You’ll learn not only how to implement algorithms but why they work, gaining insight into model evaluation and hyperparameter tuning. This book suits Python developers aiming to master machine learning fundamentals and advanced techniques, while those seeking only introductory Python might find it dense.
This AI-powered book on Python security toolkit develops a systematic approach with frameworks that adapt to your specific cybersecurity background. The content adjusts based on your proficiency and ethical hacking goals, addressing nuanced challenges encountered in security assessments. It bridges the gap between theory and practical tool development, created after you specify your areas of interest and experience level. The book emphasizes strategies for automating penetration testing and crafting custom scripts, providing a tailored approach to mastering Python for cybersecurity.
TailoredRead AI creates personalized nonfiction books that adapt to your unique background, goals, and interests. Instead of reading generic content, you get a custom book written specifically for your profession, experience level, and learning objectives. Whether you're a beginner looking for fundamentals or an expert seeking advanced insights, TailoredRead crafts a book that speaks directly to you. Learn more.
This AI-curated book on Python security toolkit development provides a tailored framework for building ethical hacking tools using Python. It focuses on methodologies for vulnerability assessment, penetration testing automation, and exploit development, all adjusted to individual proficiency and industry context. The book covers scripting techniques for network scanning, packet analysis, and security auditing, presenting strategies that fit your specific cybersecurity environment and goals. By cutting through generic advice, it offers a personalized approach that aligns with your technical background and ethical hacking aspirations, ensuring practical implementation. It thoroughly examines Python libraries and frameworks essential for cybersecurity tool creation, emphasizing hands-on applications.
Kirk Borne, Principal Data Scientist at Booz Allen and PhD Astrophysicist, highlights this book as a key resource during his work in data science, sharing "✨🎉🌟Must see this >> Free Python DataScience Coding book series for DataScientists..." This endorsement comes from someone deeply embedded in data science and big data analytics, showing how the book supports professionals striving to enhance their Python skills for research and practical problem-solving. Also, Adam Gabriel Top Influencer, an AI expert and machine learning engineer, echoes this enthusiasm, reinforcing the book's value for those tackling machine learning and data science challenges in Python.
“✨🎉🌟Must see this >> Free Python DataScience Coding book series for DataScientists ...via DataScienceCtrl Go to ——————— #abdsc #BigData #MachineLearning #AI #DeepLearning #BeDataBrilliant #DataLiteracy” (from X)
Jake VanderPlas is a software engineer at Google Research, working on tools that support data-intensive research. He creates and develops Python tools for use in data-intensive science, including packages like Scikit-Learn, SciPy, AstroPy, Altair, JAX, and many others. He participates in the broader data science community, developing and presenting talks and tutorials on scientific computing topics at various conferences in the data science world.
Drawing from his role as a software engineer at Google Research and his deep involvement with key Python packages like Scikit-Learn and AstroPy, Jake VanderPlas offers a detailed guide that brings together essential Python tools for data science in one volume. You get a hands-on understanding of libraries such as NumPy for array manipulation, pandas for data frames, Matplotlib for visualizations, and Scikit-Learn for machine learning algorithms, all explained with practical examples like using DataFrames for labeled data or building models with Scikit-Learn. This book suits anyone comfortable with Python who wants to solve everyday data challenges, whether cleaning data or crafting statistical models—it’s a solid reference rather than a beginner’s tutorial.
Pratham Prasoon, a self-taught programmer deeply involved with blockchain and machine learning, found this book invaluable during his explorations of advanced deep learning concepts. He remarks, "The Deep Learning with Python book is more advanced than the previous books. It explains the theory and best practices of deep learning with TensorFlow intuitively and practically. You'll learn about natural language processing, generative models, and more." His experience underscores the book’s balance between theory and application. Similarly, Nadim Kobeissi, a cryptography expert and NYU Paris professor, praises the book as "absolutely amazing," highlighting its broad appeal across technical domains.
Self-taught programmer, blockchain and ML enthusiast
“The Deep Learning with Python book is more advanced than the previous books. It explains the theory and best practices of deep learning with TensorFlow intuitively and practically. You'll learn about natural language processing, generative models, and more.” (from X)
François Chollet is a software engineer at Google and the creator of the Keras deep-learning library. He has made significant contributions to the field of machine learning and artificial intelligence, particularly in the development of deep learning frameworks. His work emphasizes practical applications and intuitive understanding of complex concepts, making him a respected figure in the tech community.
When François Chollet developed Keras, he aimed to simplify deep learning for Python users, and this second edition of his book reflects that mission by blending foundational theory with hands-on techniques. You’ll explore how to build neural networks from scratch, apply deep learning to image and text data, and even generate new content through neural style transfer—all explained with clear code examples and color illustrations. The book suits you if you have intermediate Python skills but are new to machine learning frameworks like TensorFlow and Keras. Chapters like "Advanced deep learning for computer vision" and "Generative deep learning" deliver concrete skills that bridge theory and practice without overwhelming jargon.
Eric Matthes is a high school science and math teacher in Alaska who has been programming since childhood. His experience teaching introductory Python courses inspired this book, focusing on making programming accessible and practical for beginners. Matthes writes software that targets inefficiencies in education, bringing open source benefits to learners. This background shapes a guide that helps you quickly gain programming skills through hands-on projects and clear explanations.
Eric Matthes is a high school science and math teacher living in Alaska, where he teaches an introductory Python course. He has been writing programs since he was five years old. Eric currently focuses on writing software that addresses inefficiencies in education and brings the benefits of open source software to the field of education. In his spare time he enjoys climbing mountains and spending time with his family.
When Eric Matthes first realized how many beginners struggle to find a clear, practical Python introduction, he wrote this book to bridge that gap. You’ll learn foundational programming concepts like variables, loops, and classes, then apply them by building projects such as a Space Invaders–style game and interactive data visualizations, making abstract ideas concrete. Matthes’s background as a high school science and math teacher shines through in the approachable explanations and hands-on exercises, ideal if you want to move quickly from theory to real coding. This book suits absolute beginners eager to write functional Python programs without getting overwhelmed by jargon or unnecessary complexity.
François Chollet is a software engineer at Google and creator of the Keras deep-learning library. His work on TensorFlow and research in computer vision and machine learning underpin this book, which introduces deep learning through Python and practical examples. Chollet’s contributions to major AI conferences ensure that his guidance is rooted in current research and real-world applications, making this book a valuable resource for developers looking to deepen their understanding of AI technologies.
François Chollet is a software engineer at Google and creator of Keras. He works on deep learning and has contributed to the TensorFlow framework. His research focuses on computer vision and machine learning applications. Chollet's papers have been published at major conferences, including CVPR and NIPS.
When François Chollet first realized how transformative deep learning could be, he set out to create a resource that demystifies this complex field using Python and his own Keras library. This book guides you through foundational concepts like neural networks and machine learning fundamentals, advancing into practical applications such as computer vision and natural language processing. You’ll engage with real examples—from image classification to text generation—that build your hands-on skills and deepen your understanding. It’s ideal if you have intermediate Python knowledge and want a clear path into deep learning without being overwhelmed by theory or jargon.
Best for learners needing data science fundamentals
Audiobook version not available
Thorsten Heller, CEO at GreenbirdIT and expert in energy data transformation, recommends this book as "The Best book to start your DataScience journey." His endorsement carries weight given his leadership in digital transformation for utilities. Heller appreciates how this book helped him solidify fundamentals during his work in data-driven energy solutions, highlighting its practical approach to Python and foundational data science concepts.
Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence. Previously he worked as a software engineer at Google and a data scientist at several startups. He lives in Seattle, where he regularly attends data science happy hours.
What if everything you knew about data science was wrong? Joel Grus argues that truly mastering data science requires more than just knowing libraries and tools—you need to understand the underlying principles. Drawing from his experience as a research engineer at the Allen Institute for AI and a former Google software engineer, Grus walks you through core concepts like linear algebra, statistics, and probability, grounding them in Python implementations. You’ll build foundational skills in machine learning algorithms, natural language processing, and data manipulation, with chapters dedicated to decision trees, neural networks, and recommender systems. This book suits those with some programming background aiming to grasp the math behind data science, rather than just applying pre-built packages.
The ten books featured here reveal distinct threads in Python mastery: automation for efficiency, deep learning for AI, security for ethical hacking, and data science for informed decisions. If you’re just starting, "Python Crash Course" offers a hands-on path to foundational skills. For those eager to dive into machine learning, combining "Python Machine Learning" with "Machine Learning with PyTorch and Scikit-Learn" accelerates your expertise.
Facing specific challenges like automating repetitive workflows? "Automate the Boring Stuff with Python" will transform your approach. Curious about writing more elegant, maintainable code? "Effective Python" sharpens your skills with actionable tips. Once you've absorbed these expert insights, create a personalized Python book to bridge the gap between general principles and your specific situation.
Whether your goal is to build AI applications, automate tasks, or enhance security knowledge, these books provide trustworthy guidance. Dive in with confidence, and let Python open new doors in your programming journey.
Frequently Asked Questions
I'm overwhelmed by choice – which Python book should I start with?
Start with "Python Crash Course, 3rd Edition" if you're new to coding. It offers clear, project-based learning that helps you build confidence quickly.
Are these books too advanced for someone new to Python?
Not at all. Books like "Automate the Boring Stuff with Python" and "Python Crash Course" are designed for beginners, while others cater to intermediate and advanced users.
What's the best order to read these Python books?
Begin with fundamentals in "Python Crash Course," then explore automation or data science depending on your interest. Advanced topics like deep learning come later.
Do these books assume I already have experience in Python?
Some do, such as "Effective Python" and "Deep Learning with Python, Second Edition." Beginners should start with entry-level titles before tackling these.
Which book gives the most actionable advice I can use right away?
"Automate the Boring Stuff with Python" is packed with practical projects you can apply immediately to save time and increase productivity.
How can I get Python learning content tailored to my specific needs?
While expert books provide solid foundations, personalized books can focus on your goals and skill level. Try creating a personalized Python book for targeted learning that complements these recommendations.
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