In the latest weekly issue, 80% of readers thought Python was the best programming language, and there were many similar questions, such as how to get started with Python? How to get started with Python in 3 months? Although there are many ways to learn Python, you still need to accumulate professional books in order to lay a solid foundation and systematically learn Python knowledge.
Who will be the first development language in the era of AI and big data?
This should have been a matter beyond debate. If three years ago, Matlab, Scala, R, Java, and Python all had their chance and the picture was not clear, three years later, the trend is very clear, especially after Facebook opened PyTorch two days ago. Python’s position as the top language in the AGE of AI is pretty well established, and it’s only a matter of who comes in second.
Learning Python is a long way off. Learning Python is undoubtedly a shortcut to a high salary if you want to enter the hot field of artificial intelligence.
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Python Core Programming, Version 3
The best-selling classic Python basics tutorial tutorial is the advanced book
Both Python2 and Python3
Machine learning data processing web crawler popular programming language
Python developers always have a desk
Book purchase: Python Core Programming (3rd edition) [Special case products do not participate in every 100 minus 50 promotion] (Wesley Chun) [Summary review test read] – JINGdong Books
Try reading: Python Core Programming (3rd edition) – Books – Asynchronous Community
The editors recommend
Comprehensive coverage of many areas of application development today excellent practice for intermediate Python developers Covers a number of practical code cases the problem sets at the end of each chapter help reinforce what you have learned
Want to improve your Python programming skills? Take a deep look at a number of related topics used in real applications ranging from regular expressions, Internet/ network programming, GUI, SQL/ database /ORM, multithreading, Web development to understand the current development area, Google, Twitter, MongoDB, OAuth, Python 3 Migration, Java/Jython include new content about Django, Google App Engine, CSV/JSON/XML, and Microsoft Office. Includes Python 2 and Python 3 code for immediate use, provides code snippets, interactive cases, and practical exercises designed to consolidate Python skills
Get Started with Python Programming and Automate Tedious Tasks
Introduction to Python programming
Python3 Field guide
Get you up to speed on efficient Python programming
Trying to read:Get started with Python Programming – Automate tedious work – book – Asynchronous community
Buy booksGet started with Python Game Programming, version 4
The editors recommend
In this book, you will learn to program in Python to do hours of manual work in minutes without prior programming experience. Once you’ve mastered the basics of programming, you can effortlessly create Python programs for efficient automation, including:
● Search text in a file or files;
● Create, update, move and rename files and folders;
● Search the web and download online content;
● Update and format data in an Arbitrary size Excel spreadsheet;
● Split and merge PDF files, and how to watermark and encrypt;
● Send reminder email and text notification;
● Fill out the online form. Al Sweigart is a prominent Python programmer. He is also the author of Programming Python Cryptography, Getting Started with Python Game Programming, and The Python and Pygame Game Development Guide.
Learning Python the “Stupid Way” (3rd Edition)
Enjoy the full 5-hour video tutorial
Learn the basics of Python programming with hundreds of thousands of Python tutors
It lays a solid foundation for Web development actual combat data analysis
Trying to read:If you’re a Python buff, you’ve probably read these books — Asynchronous Communities
Books:Learning Python the “Stupid Way” (version 3 with 1 CD)
The editors recommend
Zed Shaw has perfected this worldGood Python learning system. Just follow along and you will be as successful as the hundreds of thousands of beginners Zed has taught so far.
In this book, you will learn Python by completing 52 well-designed problem sets. Read the exercises and write the code for them exactly (no copy and paste!). , correct your errors, and watch the program in action. Along the way, you’ll learn how software works, what a good program looks like, how to read, write, and think about code, and how to find and fix bugs with the skills of a professional programmer.Importantly, you will learn the following initial skills necessary to write good Python software.
This book will pay off for every minute you put in. Python is one of the world’s bestStrong,One of the most popular programming languages, you’ll soon be a Python programmer.
You can also watch videos from Zed! The DVD that comes with the book contains more than five hours of passionate teaching: a complete Python video tutorial!
“Think Python Like a Computer Scientist, Version 2”
Learn to think like a computer scientist and learn Python easily
The editors recommend
• If you want to learn how to program, Python is a good place to start. The book starts with basic programming concepts and guides the reader through the Python language, moving on to higher-level concepts such as functions, recursion, data structures, and object-oriented design. The second edition of the book and its supporting code have been updated to support Python 3. The exercises at the end of each chapter will help readers deepen their understanding of the programming concepts they have just learned. This book is ideal for high school and college students, self-learners, and professionals who need to understand the basics of programming. Beginners can learn how to start Python programming in a browser.
• Start from the basics, including the syntax and semantics of the language.
• Master a clear definition of each programming concept.
• Learn values, variables, statements, functions, and data structures step by step.
• Know how to operate files and databases.
• Understanding objects, methods, and object-oriented programming.
• Use a variety of debugging techniques to fix syntax, runtime, and semantic errors.
• Explore functions, data structures and algorithms through a series of case studies. The sample code in this book is maintained in the GitHub repository and is easy to download and modify.
Advanced Programming in Python, Version 2
Python advanced Machine Learning Artificial intelligence Deep learning development popular programming language Web development and Backend Engineers Reference Guide written based on Python3.5
Books:Think Like a computer scientist in Python version 2
Trying to read:If you’re a Python buff, you’ve probably read these books — Asynchronous Communities
The editors recommend
Python is a dynamic programming language that is simple and powerful enough to be used in many areas. While writing Python code is relatively easy, writing code that is efficient and easy to maintain and reuse is a challenge. The focus of this book is to familiarize you with best practices, useful tools, and standards that Python professionals use every day. First, you’ll learn about the new features in Python 3.5 and quick tricks to improve productivity. Next, you’ll learn how to use the high-level and useful Python syntax elements in this new release, as well as different ways to implement metaprogramming. This book covers code management tools, methods for writing clear documentation, and test-driven development, all important elements of writing code. By learning the general principles of optimization, strategies for finding bottlenecks, and selected tools for applying optimization, you can gain a deeper understanding of how to write efficient code. By the end of this book, you’ll be an expert at writing efficient and maintainable code. By reading this book, you will be able to: learn about conventions and best practices widely adopted in the Python community; Efficiently package Python code for community and production use; A simple and lightweight way to automate code deployment on remote systems; Improve code quality, reliability and performance; Writing concurrent code in Python; Extend Python with code written in other languages.
Proficient in Python Natural Language Processing
Develop amazing NLP project natural language processing tasks in Python
Experience in designing and building applications for NLP using Python
Books:Proficient in Python natural language processing ([in India], Deepti, Chopra, Nisheeth, Joshi, Iti, Mathur) – the review of trying to read 】 【 jingdong books
Trying to read:If you’re a Python buff, you’ve probably read these books — Asynchronous Communities
The editors recommend
Natural language processing (NLP) is one of the research fields related to computational linguistics and artificial intelligence. NLP focuses on human-computer interaction, which provides a seamless interaction between computers and humans, enabling computers to understand human language with the help of machine learning. This book details how to perform a variety of natural language processing (NLP) tasks using Python, and helps you master best practices for designing and building NLP based applications using Python. This book guides the reader through the application of machine learning tools to develop a wide variety of models. This book provides a clear introduction to the creation of training data and the implementation of major NLP applications, such as named entity recognition, question answering system, discourse analysis, word sense disambiguation, information retrieval, sentiment analysis, text summarization, and coreference resolution. This book helps readers create NLP projects using NLTK and become experts in the field. By reading this book, you will be able to:
● String matching algorithm and standardized technology;
● Statistical language modeling technology;
● Have a deep understanding of the development of stem extractors, shape reducers, morphology analyzers and morphology generators;
● Develop search engine and implement pos tagging and statistical modeling (including N-gram method) and other related concepts;
● Familiar with concepts such as tree library construction, CFG construction, CYK and Earley line graph parsing algorithm;
● Develop NER based systems and understand and apply sentiment analysis concepts;
● Understand and implement relevant concepts such as information retrieval and text summarization;
● Develop a discourse analysis system and a system based on coreference resolution.
Python Machine Learning Practice Guide
Immersive writing style
Easy to master the actual machine learning knowledge
Apply advanced machine learning methods to solve everyday problems
Book purchase: A Practical Guide to Python Machine Learning (Alexander,T.,Combs
Try reading: If you’re a Python hotshot, you’ve probably read these books – Asynchronous Communities
The editors recommend
Machine learning is fast becoming a bi standby module for a data-driven world. Many different fields, such as robotics, medicine, retail and publishing, depend on this technology. By reading the Python Machine Learning Practice Guide, you will learn how to build real machine learning applications step by step. The Python Machine Learning Practice Guide teaches you how to use machine learning to collect, analyze, and manipulate large amounts of data in an easy-to-understand, concise way. Through easy-to-understand projects, this book explains how to work with various types of data and how and when to apply different machine learning techniques, including supervised and unsupervised learning. Each project in this book offers both teaching and practice. You’ll learn how to use clustering techniques to find low airfares and how to use linear regression to find a cheap apartment. The Python Machine Learning Practice guide is intended for Python programmers, data scientists, architects who understand data science, and people who want to build complete Python-based machine learning systems. By reading the Python Machine learning Practice Guide, you will: · Understand the Python machine learning ecosystem; · Understand how to perform linear regression; · Introduction of machine vision concepts; · Advanced data visualization technology; · How to deploy machine learning models using third-party apis; · Modeling technology of time series; · How to build an unsupervised model.
Writing web crawlers in Python
Data processing and data mining in Python
The realization principle of web crawler technology is analyzed
Proficient in Python web crawlers
Books:”Write web crawlers in Python” (Richard Lawson) – JINGdong Books
Trying to read:If you’re a Python buff, you’ve probably read these books — Asynchronous Communities
The editors recommend
As a convenient way to collect information on the Internet and extract usable information from it, web crawler technology is becoming more and more useful. With a simple programming language like Python, you can crawl complex websites with a small amount of programming skill. Writing a Web Crawler in Python is an excellent guide to crawling web data using Python, explaining crawling data from static pages and using caching to manage server load. In addition, the book covers how to use AJAX urls and Firebug extensions to crawl data, as well as more facts about crawl techniques such as using browser rendering, managing cookies, and extracting data from complex sites protected by captch-code by submitting forms. This book uses Scrapy to create an advanced web crawler and some real web crawlers. Writing a Web crawler in Python covers: crawling websites by following links; Extract data from the page using LXML; Build thread crawlers to crawl pages in parallel; Caches downloaded content to reduce bandwidth consumption; Parsing web sites that rely on JavaScript; Interact with forms and sessions; Solve the captcha problem of protected pages; Reverse engineering AJAX calls; Create advanced crawlers using Scrapy. This book is written for developers who want to build reliable data crawl solutions, and some experience with Python programming is assumed. Of course, readers with experience developing other programming languages can also read this book and understand the concepts and principles involved.
“Python Object-oriented Programming Guide
Master the essence of Object-oriented programming in Python
Build powerful real-world applications
Books:A Guide to Object-oriented Programming in Python by Steven F. Lott
Trying to read:If you’re a Python buff, you’ve probably read these books — Asynchronous Communities
The editors recommend
This book is dedicated to providing in-depth insight into the advanced features of the Python language, providing step-by-step instructions on how to write high-quality Python code with rich, powerful code examples. This book is a must-read for becoming a Master Python programmer. This book introduces the concept of object-oriented programming in Python through practical examples. It provides detailed examples of all the special methods you can use to seamlessly integrate Python’s built-in features, and shows you how to use JSON, YAML, Pickle, CSV, XML, Shelve, and SQL to create persistent objects and transfer them between processes. It also covers Logging and Warning modules, unit tests, configuration files, and how to use the command line. The book is divided into three main sections: Implementing Python-style classes in special ways; Persistence and serialization; Test, debug, deploy, and maintain. The special methods section is further divided into initialization methods, basic special methods, property access, callable objects, contexts, containers, collections, values, and advanced techniques such as decorators and mixin classes. This book is rich in examples. It introduces the concept of object-oriented programming in Python through many practical examples, which will help readers better grasp the advanced features of Python and write better practical applications.
Building Machine Learning Applications with NLTK and Python Libraries
NLTK and Python libraries build machine learning applications
Books:NLTK Basics Tutorial Builds machine learning applications using NLTK and Python libraries
Trying to read:If you’re a Python buff, you’ve probably read these books — Asynchronous Communities
The editors recommend
Natural language processing (NLP) is an interdisciplinary field of artificial intelligence and computational linguistics. It deals with the interaction between computer and human language. With the increasing demand for human-computer interaction, it has become an inevitable trend for computers to have the ability to process major natural languages. NLTK is a powerful and robust toolkit in this area. In this book, we will first introduce some knowledge related to NLP. Then, we’ll explore some data science related tasks to learn how to build custom identifiers and parsers from scratch. Along the way, we’ll delve deeper into the basic concepts of the NLP space and provide practical insights into the various open source Python tools and libraries in the space. Next, we will introduce how to analyze social media websites, find hot topics and conduct public opinion analysis. After that, we’ll look at some of the tools available for processing large amounts of text. By the end of this book, you will have a good understanding of the concepts in NLP and data science and be able to apply this knowledge to your daily work. If you are a fan of NLP or machine learning-related fields and have some experience with text processing, then this book is for you. In addition, the book is ideal for professional Python programmers to quickly learn the NLTK library. Through this book, you will learn: ■ Understand the complexities of natural languages and how machines process them. ■ How to use identification processing to clear up text ambiguity and use chunking to better process data. ■ Explore the role of different tag types and learn how to tag sentences. ■ How to create custom parsers and identifiers based on your needs. ■ How to build utilities with spell checking, search, machine translation, and question answering systems. ■ How to retrieve relevant data content by means of information crawl and capture. ■ How to build a classification system for different texts through feature extraction and selection. ■ How to use various third-party Python libraries, such as pandas, SciKit-learn, Matplotlib, and Gensim ■ How to analyze social media sites, including hot topics, public opinion analysis, etc.
High Performance Programming in Python
Deep understanding of Python implementations
Make your Python code run faster
Books:High Performance Programming in Python (Micha,Gorelick, Gorelick, Ian,Ozsvald, Ozhvold) [Abstract Book Review] – JD Books
Trying to read:If you’re a Python buff, you’ve probably read these books — Asynchronous Communities
The editors recommend
It’s not enough for Python code to run correctly; you need to make it run faster. This book helps you gain a deeper understanding of Python implementations by exploring the underlying theory behind design decisions. You’ll learn how to find performance bottlenecks and how to significantly speed up code in applications with large data volumes. How can you take advantage of multi-core architecture or clustering? How do you build a system that scales without losing reliability? Experienced Python programmers will learn concrete solutions to these and other problems, as well as success stories from companies that use high-performance Python programming in social media analytics, productized machine learning, and other scenarios. By reading this book, you will be able to: ■ Get a better grasp of Numpy, Cython, and profilers; ■ Understand how Python abstracts the underlying computer architecture; ■ Use profiling to find bottlenecks in CPU time and memory usage; ■ Accelerate matrix and vector calculations by selecting suitable data structures to write efficient programs ■ Use tools to compile Python into machine code ■ Manage concurrent multiple I/O and computing operations ■ Convert multi-process code to run on a local or remote cluster ■ Solve large problems with less memory.
“Despite Python’s popularity in academic and industrial circles, people often abandon Python programs because they run too slowly. This book eliminates this misrepresentation by providing a comprehensive overview of strategies for improving and optimizing The speed and extensibility of Python computation.”
— Jake VanderPlas, Washington University
Beginner’s Guide to Python Physical Modeling
A practical guide to solving scientific problems with Python, recommended by professors at many of the world’s leading universities
Trying to read:If you’re a Python buff, you’ve probably read these books — Asynchronous Communities
Editor’s Recommendation:
This book is designed to help Python learners acquire sufficient Python programming skills for physical modeling. The book is divided into 8 chapters and 5 appendices, including basic Python knowledge, data structure and program control, data input and output, Advanced Python knowledge and advanced technology, etc., which runs through three physical modeling experiments in different directions and difficulties. The appendix section covers Python installation, error messages, version differences, and topics for further study.
This book is suitable for beginners to Python, especially those who want to use Python for scientific computation and physical modeling.
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