The Examination Center of the Ministry of Education recently issued a notice on the adjustment of the National Computer Rank (NCRE) system, and decided to add "Python Language Programming" to the National Computer Level 2 Examination from March 20 18.

Nine months ago, the information technology curriculum reform plan of Zhejiang Province was promulgated, and Python decided to enter the information technology textbooks of Zhejiang Province. From 20 18, the programming language of information technology textbooks in Zhejiang Province will be changed from vb to Python.

Pupils have started to learn Python. God, it must be right to learn Python after reading these.

Amway yibo book list

Introduction to Python

Quick Introduction to Python Programming —— Automating tedious work.

Author: American Al Swigat (Svegat)

Python3 programming from introduction to practice

Amazon sells Python programming books.

This book is a practical guide to Python programming oriented to practice. This book not only introduces the basic knowledge of Python language, but also teaches readers how to apply these knowledge and skills through project practice. The first part of this book introduces the basic concepts of Python programming, and the second part introduces some different tasks. By writing Python programs, computers can do it automatically. Each chapter of the second part has some project procedures for readers to learn. At the end of each chapter, there are some exercises and in-depth practical projects to help readers consolidate what they have learned. The appendix provides the answers to all the exercises.

Learn Python by Stupid Method (3rd Edition)

Author: Zed A. Shaw, USA

Learn Python by Stupid Method (3rd Edition) is an introductory Python book, suitable for readers who don't know much about computers and have never studied programming, but are interested in programming. This book guides readers to learn programming step by step by means of exercises, from simple printing to the realization of complete projects, so that beginners can start with basic programming techniques and finally experience the basic process of software development.

The structure of "Learn Python by Stupid Method (3rd Edition)" is very simple, * * * includes 52 exercises, 26 of which cover three topics of input/output, variables and functions, and the other 26 cover some advanced topics, such as conditional judgment, loops, classes and objects, code testing and project implementation. The format of each chapter is basically the same. Start with the code practice, write the code according to the instructions, run and check the results, and then do additional exercises.

Beginner's Guide to Python Programming

Author: Beautiful Michael Dawson

Beginners' Guide to Python Programming tries to help beginners master Python language and programming skills in a relaxed and interesting way. This book *** 12 chapters, each chapter will use a complete game to demonstrate key knowledge points. Learning programming by writing fun little software will arouse readers' interest and reduce learning difficulty. At the end of each chapter, the knowledge points of this chapter will be summarized and some small exercises will be given for readers to try their hand. The author skillfully embeds all the programming knowledge into these examples, and truly makes education entertaining.

Data structure (Python language description)

Author: Kenneth A. Lambert

In computer science, data structure is an abstract and difficult advanced course. Python language has simple syntax and strong interactivity. Using Python to explain topics such as data structure is simpler and clearer than C language.

Chapter 1 of this book briefly introduces the basic knowledge and characteristics of Python language. Chapter two to chapter four introduce abstract data types, data structures, complexity analysis, arrays and linear linked list structures in detail. The fifth and sixth chapters focus on the related knowledge of object-oriented design. Chapter 5 includes the main differences between interface and implementation, polymorphism and information hiding. Chapter 6 mainly explains the related knowledge of inheritance. Chapters 7 to 9 introduce the related knowledge of linear sets represented by stacks, queues and linked lists. Chapter 10 introduces various tree structures, chapter 1 1 explains the related contents of sets and dictionaries, and chapter 12 introduces graphs and graph processing algorithms. Review questions and case studies are given at the end of each chapter to help readers consolidate and think.

Think Python like a computer scientist.

Author: Allen B Downey, USA

This book teaches Python programming according to the idea of training readers to think like computer scientists. The main body of the book is how to think, design and develop, and the specific programming language only provides a convenient medium to introduce specific scenes. This is not a book about languages, but a book about programming ideas. Different from other programming language books, it does not stick to language details, but tries to guide readers to become better with vivid examples and rich exercises from the perspective of beginners.

Python advanced edition

Python Advanced Programming (2nd Edition)

Author: Micha, Poland? Jaworski (Jaworski), Tariq Zeyad (Ryder)

Based on Python version 3.5, this book profoundly reveals the advanced skills of Python programming through the contents of chapter 13. Starting with the introduction of Python language and its community, this book gives a comprehensive and systematic explanation of Python syntax, naming rules, Python package writing, code deployment, extended program development, code management, document writing, test development, code optimization, concurrent programming, design patterns and other important topics.

This book is suitable for readers who want to further improve their Python programming skills, and also for readers who are interested in Python programming. Combining typical and practical development cases, this book can help readers create high-performance, reliable and maintainable Python applications.

Python high performance programming

Authors: Gorelick, Ozsvald.

This book * * * consists of 12 chapters, focusing on how to optimize the code and speed up the running speed of practical applications. This book mainly contains the following topics: background knowledge of computer internal structure, lists and tuples, dictionaries and collections, iterators and generators, matrix and vector calculation, concurrency, clustering and work queues. Finally, through a series of real cases, the problems needing attention in application scenarios are demonstrated.

This book is suitable for junior and intermediate Python programmers, as well as readers who have a certain Python language foundation and want to be advanced and improved.

Python geek project programming

Author: Mahesh Venkitachalam, USA

Python is a high-level programming language with explanatory, object-oriented and dynamic data types. Through Python programming, we can solve many tasks in real life.

This book helps and encourages readers to explore the world of Python programming through 14 interesting projects. Chapter *** 14 of this book introduces some interesting projects realized by Python programming, including parsing iTunes playlist, simulating artificial life, creating ASCII code art drawings, splicing photos, generating 3D drawings, creating particle simulation fireworks fountain effect, realizing 3D light projection algorithm, and combining hardware such as Arduino and raspberry pie with Python. This book does not introduce the basic knowledge of Python language, but shows how to use Python to solve various practical problems and how to use some popular Python libraries through a series of complex projects.

Python Core Programming (3rd Edition)

Author: Wesley Chun, USA

This book is a brand-new upgraded version of the classic bestseller Python Core Programming (second edition), which is divided into three parts. 1 explains some general applications of Python, including regular expressions, network programming, Internet client programming, multithreading programming, GUI programming, database programming, Microsoft Office programming, extended Python and so on. The second part explains the topics related to Web development, including Web client and server, Web programming related to CGI and WSGI, Diango Web framework, cloud computing and advanced Web services. The third part is the supplementary/experimental chapter, including some contents such as text processing.

This book is suitable for experienced Python developers.

Python machine learning-the core algorithm of predictive analysis "

Author: Michael Bowles

When learning and studying machine learning, faced with dazzling algorithms, novices of machine learning are often at a loss. This book helps readers understand machine learning from the perspective of algorithm and Python language implementation.

This book focuses on two core "algorithm families", namely, penalty linear regression and integral method, and shows the principle of using the discussed algorithm through code examples. The book is divided into seven chapters, and discusses in detail the two core algorithms of the prediction model, the construction of the prediction model, the concrete application and realization of the penalty linear regression and integral method.

Python machine learning practice guide

Author: Alexander T. Combs, USA

Machine learning is an increasingly popular field in recent years, and Python language has gradually become one of the mainstream programming languages after a period of development. This book combines the two hot fields of machine learning and Python language, and uses two core machine learning algorithms to maximize the advantages of Python language in data analysis.

This book has 10 chapters. Chapter 1 explains the Python machine learning ecosystem, and the remaining nine chapters introduce many algorithms related to machine learning, including various classification algorithms, data visualization technologies, recommendation engines and so on. , mainly including the application of machine learning in apartments, air tickets, IPO market, news source, content promotion, stock market, pictures, chat robots and recommendation engines.

Proficient in Python natural language processing

Authors: Deepti Chopra, Nisheeth Joshi, Iti Mathur, India.

Natural language processing is one of the fields related to human-computer interaction in computational linguistics and artificial intelligence.

This book is a comprehensive learning guide for learning natural language processing, and introduces how to realize various NLP tasks with Python to help readers create projects based on real-life applications. Chapter *** 10 of this book covers such topics as string manipulation, statistical language modeling, morphology, part-of-speech tagging, grammatical analysis, semantic analysis, emotional analysis, information retrieval, discourse analysis and NLP system evaluation.

This book is suitable for readers who are familiar with Python language and have a certain understanding and interest in natural language processing and development.

Python data science guide

Author: Indian Sabramanian (Sabramanian)

More than 60 practical development skills to help you explore Python and its powerful data science functions.

Python, as a high-level programming language, has become a highly respected language in the field of programming because of its simplicity, readability and extensibility, and has become one of the first choices for data scientists.

This book introduces the application of Python in data science in detail, including data exploration, data analysis and mining, machine learning, large-scale machine learning and other topics. Each chapter provides enough mathematical knowledge and code examples for readers to understand the algorithm functions of different depths and help readers better grasp various knowledge points.

This book has a clear content structure and complete examples, which will benefit both novices and experienced data scientists in the field of data science.

Writing Web Crawler in Python

Author: Australian Richard Lawson (Richard Lawson)

This book explains how to use Python to write a web crawler program, including the introduction of web crawler, three ways to capture data from pages, extracting data from cache, using multithreading and processes for concurrent capture, how to capture content from dynamic pages, interacting with forms, dealing with verification code problems in pages, and capturing data using Scarpy and Portia. Finally, several real websites are captured by using the data capture technology introduced in this book, aiming at helping readers learn and live.

This book is suitable for readers who have some Python programming experience and are interested in reptile technology.

Bayesian Thinking: Python Learning Method for Statistical Modeling

Author: Allen B Downey, USA

This book helps those who want to solve practical problems with mathematical tools. The only requirement may be a little knowledge of probability and programming. Bayesian method is a commonly used mathematical method to solve uncertain problems by using probability knowledge. A computer professional should be familiar with its application in machine translation, speech recognition, spam detection and other common computer problems.

Python natural language processing

Authors: Steven Bird, Ivan Klein, Edward Lopez.

Natural language processing is an important direction of computer science and artificial intelligence. It studies all kinds of theories and methods to realize effective communication between people and computers with natural language, involving all the operations of computers on natural language.

Python natural language processing is a practical introductory guide in the field of natural language processing, which aims to help readers learn how to write programs to analyze written languages. Python natural language processing is based on Python programming language and an open source library of natural language toolkit named NLTK, but readers are not required to have Python programming experience. *** 1 1 chapters in this book are arranged in order of difficulty. 1 Chapter 3 introduces the basic knowledge of language processing and tells how to analyze interesting text information with small Python programs. Chapter 4 discusses structured programming to consolidate the programming points introduced in previous chapters. Chapters 5 to 7 introduce the basic principles of language processing, including labeling, classification and information extraction. Chapters 8 to 10 introduce sentence parsing, syntactic structure recognition and sentence meaning expression methods. Chapter 1 1 introduces how to manage language data effectively. The postscript briefly discusses the past and future in the field of natural language processing.

This book is very practical, including hundreds of practical examples and grading exercises. It can be used by readers for self-study, can be used as a teaching material for natural language processing or computational linguistics, and can also be used as a supplementary reading material for courses such as artificial intelligence, text mining and corpus linguistics.

Python data analysis "

Author: Ivan Idris, Indonesia

Python is a multi-paradigm programming language, which is suitable for both object-oriented application development and functional design mode. Python has become an ideal programming language for data scientists for data analysis, visualization and machine learning, which can help you improve your work efficiency quickly.

This book will guide beginners to be familiar with all aspects of Python data analysis, from data retrieval, cleaning, operation, visualization and storage to advanced analysis and modeling. At the same time, this book focuses on a series of open source Python modules, such as NumPy, SciPy, matplotlib, pandas, IPython, Cython, scikit-learn and NLTK. In addition, this book also introduces topics such as data visualization, signal processing, time series analysis, database, predictive analysis and machine learning. By reading this book, you will become an expert in data analysis.