This is how middle school students can learn python. Recommended books for middle school students to learn python.

Hello everyone, the editor will answer the question of whether it is necessary for junior high school students to spend money to learn python. Many people don’t know that middle school students can learn python.pdf in this way, let’s take a look now!

python introductory tutorial (very detailed)

The introductory tutorial for python is as follows: Preparation materials: Windows computer, python1. Here is a simple calculation program written in python software about the discount on the selling price of goods. First open the python software.

2. After entering python, the interface as shown in the picture will appear. Follow the arrow instructions in the picture, first select the File option, and then select the New file option in the drop-down menu to install the Python interpreter . 3. After the selection is completed, a new interface will appear, as indicated by the arrow and red box in the figure.

4. Enter this new interface and enter the program you want to edit. As shown in the figure, it is a simple calculation program for discounting the selling price of goods.

5. After the program input is completed, follow the arrows and red box instructions in the picture, first select the Run option, and then select Run Module in the drop-down menu (Note: In addition to this method, you can also click F5 on the keyboard).

6. At this time, the words as shown in the picture will appear on the original interface. This is because the program has been edited. At this time, you can enter a number, and then press Enter, and you will be asked to enter a discount. After entering, you can get the final sales price. price results.

7. As shown in the figure, the original price entered here is 10 and the discount is 0.2, so the system calculates the price after discount as 2 according to the written program.

Google Artificial Intelligence Writing Project: Xiaofamao

python introductory tutorial (very detailed)

The introductory tutorial for python is as follows: Preparation materials: Windows computer, python1. Here is a simple calculation program written in python software about the price discount of goods. First, open the python software type and what is there .

2. After entering python, the interface as shown in the picture will appear. Follow the arrow instructions in the picture, first select the File option, and then select the New file option in the drop-down menu. 3. After the selection is completed, a new interface will appear, as indicated by the arrow and red box in the figure.

4. Enter this new interface and enter the program you want to edit. As shown in the figure, it is a simple calculation program for discounting the selling price of goods.

7. As shown in the figure, the original price entered here is 10 and the discount is 0.2, so the system calculates the price after discount as 2 according to the written program.

python introductory tutorial (very detailed)

Beginners need to master the installation and use of the programming environment, the application of input and output statements, the use of operational expressions, etc. The specific tutorials are as follows: 1. Installation and use of programming environment. For example, for learning Python, IDLE that comes with the software is generally recommended, as it is simple and easy to use.

Figure 1 2. Master the use of input and input sentences. Input statements let the computer know what you entered through the keyboard, and output statements let you know the results of the computer's execution. Take the output statement as an example: the content in "" is output as is, and multiple output items are separated by,.

Example 3. Master the use of operation (including calculation and logic) expressions. This mainly uses +, -, *, /, (), >, =,

How can someone without any foundation get started with Python?

Python is a computer programming language. You may have heard of many popular programming languages, such as the very difficult C language, the very popular Java language, the Basic language suitable for beginners, the JavaScript language suitable for web programming, etc.

So what kind of language is Python? First, let’s popularize the basic knowledge of programming languages.

The purpose of developing programs in any programming language is to let the computer do things, such as downloading an MP3, writing a document, etc. The CPU that does the computer work only understands machine instructions. Therefore, although different programming languages are very different, in the end All have to be "translated" into machine instructions that the CPU can execute.

Different programming languages have great differences in the amount of code written to do the same job. For example, to complete the same task, you need to write 1,000 lines of code in C language, only 100 lines of code in Java, and only 20 lines in Python. So Python is a fairly high-level language.

You may ask, is it not good to have less code? The price of less code is slow running speed. A C program may take 1 second to run, a Java program may take 2 seconds, and a Python program may take 10 seconds. Does that mean that the lower-level programs are more difficult to learn, and the higher-level programs are easier?

On the surface, yes, but in very high-abstraction computing, advanced Python programming is also very difficult to learn, so high-level programming languages do not mean simplicity. However, Python language is very simple and easy to use for beginners and to complete common tasks.

Even Google is using Python on a large scale, so you don’t have to worry about learning it being useless. What can you do with Python?

It can do daily tasks, such as automatically backing up your MP3s; it can build websites, and many famous websites including YouTube are written in Python; it can do the backend of online games, and the backends of many online games are developed in Python. In short, it can do many, many things.

Of course, there are things that Python cannot do, such as writing operating systems, which can only be written in C language; writing mobile applications, only using Objective-C (for iPhone) and Java (for Android); writing 3D games, it is best to use C or C++.

If you are a novice user, you meet the following conditions: you can use computers, but have never written a program; you still remember the equations and a little bit of algebra from junior high school mathematics; you want to change from a novice programming user to a professional software architect; you can do it every day Set aside an hour and a half to study. You can see the code below.

How do primary and secondary school students learn Python programming?

1. Primary and middle school students have very little time to interact with computers, so they need to operate computers frequently and be familiar with computer operations, checking information, environment variables, command lines, etc. 2. Programming requires some basic knowledge of English. You don’t need to be very good at it, but you must at least be able to understand some simple English words related to Internet development.

3. Python is a glue language. Its syntax is very simple. Most of its functions rely on frameworks. But don’t ignore it because the syntax is simple. The foundation is very, very important. After studying the basic syntax of Python, start learning the framework.

4. It is best to choose a framework you like more to learn, learn one by one, and bite off more than you can chew.

0 basic self-study python, any recommended introductory books?

The Python language used by AlphaGo is the programming language closest to AI.

The Examination Center of the Ministry of Education recently issued a notice "About the Adjustment of the National Computer Grade (NCRE) System" and decided to add the "Python Language Programming" subject to the National Computer Level 2 Examination starting from March 2018.

Nine months ago, the Zhejiang Province Information Technology Curriculum Reform Plan was released, and Python was confirmed to be included in the Zhejiang Province Information Technology textbooks. From 2018, the programming language in Zhejiang Province Information Technology textbooks will be changed from VB to Python.

Elementary school students have started to learn Python. Oh my god, you are right to learn Python after reading these.

Amway's book list Python Getting Started "Get Started Quickly with Python Programming - Automate Cumbersome Work" Author: [US] Al Sweigart (Sweigart) Python3 Programming from Getting Started to Practice Amazon's best-selling Python programming book This book is a practice-oriented book A practical guide to Python programming.

This book not only introduces the basic knowledge of the Python language, but also teaches readers how to apply these knowledge and skills through project practice.

The first part of the book introduces basic Python programming concepts, and the second part introduces a number of different tasks that can be automated by the computer by writing Python programs. Each chapter in the second part has some project procedures for readers to learn.

Some exercises and in-depth practical projects are also provided at the end of each chapter to help readers consolidate the knowledge they have learned. The appendix provides answers to all exercises.

"Learning Python the Hard Way" (3rd Edition)" Author: [US] Zed A. Shaw "Learning Python the Hard Way" (3rd Edition)" is an introductory book to Python that is suitable for people who don't know much about computers. Readers who have learned programming but are interested in programming can learn how to use it.

This book guides readers to learn programming step by step in the form of exercises, from simple printing to the implementation of a complete project, allowing beginners to start with basic programming techniques and finally experience the basic process of software development.

The structure of "Learning Python the Hard Way" (3rd Edition) is very simple and includes a total of 52 exercises, 26 of which cover the three topics of input/output, variables and functions, and the other 26 cover some more advanced topics. , such as conditional judgment, loops, classes and objects, code testing and project implementation, etc.

The format of each chapter is basically the same, starting with a coding exercise, following the instructions to write the code, running it and checking the results, and then doing additional exercises.

"Python Programming Beginner's Guide" Author: [US] Michael Dawson "Python Programming Beginner's Guide" attempts to help beginners master the Python language and programming skills in an easy and interesting way.

The book has 12 chapters in total. Each chapter will use a complete game to demonstrate the key knowledge points, and learn programming by writing fun small software to arouse readers' interest and reduce the difficulty of learning.

At the end of each chapter, the knowledge points of the chapter will be summarized, and some small exercises will be given for readers to try their skills. The author cleverly embeds all programming knowledge into these examples, making it truly entertaining and educational.

"Data Structure (Python Language Description)" Author: [US] Kenneth A. Lambert (Lambert) In computer science, data structure is an advanced course with abstract concepts and high difficulty. The syntax of Python language is simple and highly interactive.

Using Python to explain topics such as data structures is easier and clearer than C language. Chapter 1 of this book briefly introduces the basic knowledge and features of the Python language.

Chapters 2 to 4 provide a detailed introduction to abstract data types, data structures, complexity analysis, arrays and linear linked list structures. Chapters 5 and 6 focus on the relevant knowledge of object-oriented design. Chapter 5 includes The key differences between interfaces and implementations, polymorphism, information hiding, etc. Chapter 6 mainly explains the relevant knowledge of inheritance. Chapters 7 to 9 introduce the relevant knowledge of linear collections represented by stacks, queues and lists. .

Chapter 10 introduces various tree structures, Chapter 11 explains the related content of sets and dictionaries, and Chapter 12 introduces graphs and graph processing algorithms. At the end of each chapter, review questions and case studies are also given to help readers consolidate and think.

"Think Python Like a Computer Scientist" Author: [US] Allen B. Downey This book teaches Python language programming based on the idea of training readers to think like a computer scientist.

The main theme throughout the book is how to think, design, and develop, and the specific programming language only provides a medium for convenient introduction of specific scenarios. It is not a book that introduces languages, but a book that introduces programming ideas.

Unlike other programming language books, it does not stick to language details, but tries to start from a beginner's perspective, using vivid examples and rich exercises to guide readers to get better at it.

Python Advanced Python Programming (2nd Edition)" Author: [Poland] Michał Jaworski (Jaworski), [France] Tarek Ziadé (Ryder) This book is based on Python 3.5 version. Through the content of 13 chapters, Advanced techniques for Python programming are revealed in depth.

This book starts with an introduction to the current status of the Python language and its community, and covers Python syntax, naming rules, Python package writing, deployment code, extension development, management code, document writing, test development, code optimization, concurrent programming, design patterns, etc. Important topics are explained in a comprehensive and systematic manner.

This book is suitable for readers who want to further improve their Python programming skills, and is also suitable for reference study by readers who are interested in Python programming. The whole book combines typical and practical development cases to help readers create high-performance, reliable and maintainable Python applications.

"Python High-Performance Programming" Author: [US] Micha Gorelick, Ian Ozsvald This book has 12 chapters, which provide detailed explanations on how to optimize code and speed up the running speed of practical applications.

This book mainly covers the following topics: background knowledge of computer internal structures, lists and tuples, dictionaries and sets, iterators and generators, matrix and vector calculations, concurrency, clusters and work queues, etc. Finally, a series of real cases demonstrate issues that need attention in application scenarios.

This book is suitable for junior and intermediate Python programmers, readers who have a certain Python language foundation and want to advance and improve. "Python Geek Project Programming" Author: [US] Mahesh Venkitachalam Python is an interpreted, object-oriented, dynamic data Type of high-level programming language.

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.

There are 14 chapters in the book, which introduce some interesting projects implemented through Python programming, including parsing iTunes playlists, simulating artificial life, creating ASCII code art pictures, photo splicing, generating three-dimensional stereograms, creating particle simulation fireworks fountain effects, and realizing three-dimensional images. Ray casting algorithms, and electronic projects using Python with hardware such as Arduino and Raspberry Pi.

This book does not introduce the basic knowledge of the 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 non-simple projects.

"Python Core Programming (3rd Edition)" Author: [US] Wesley Chun (Wesley Chun) This book is a new upgraded version of the classic best-selling book "Python Core Programming (2nd Edition)", which is divided into 3 parts.

Part 1 explains some common applications of Python, including regular expressions, network programming, Internet client programming, multi-thread programming, GUI programming, database programming, Microsoft Office programming, extended Python, etc.

Part 2 explains topics related to web development, including web clients and servers, CGI and WSGI-related web programming, the Diango web framework, cloud computing, and advanced web services.

Part 3 is a supplementary/experimental chapter, including text processing and some other content. This book is suitable for Python developers with certain experience.

Python Machine Learning - Core Algorithm for Predictive Analysis" Author: [US] Michael Bowles (Bowles) When learning and researching machine learning, faced with dazzling algorithms, machine learning novices are often at a loss.

This book helps readers understand machine learning from the perspective of algorithms and Python language implementation. This book focuses on two core "algorithm families", namely penalized linear regression and ensemble methods, and uses code examples to demonstrate the principles of using the algorithms discussed.

The book is divided into 7 chapters, which discuss in detail the two types of core algorithms of prediction models, the construction of prediction models, and the specific application and implementation of penalized linear regression and ensemble methods.

"Python Machine Learning Practical Guide" Author: [US] Alexander T. Combs Machine learning is an increasingly popular field in recent years. At the same time, the Python language has gradually become one of the mainstream programming languages after a period of development.

This book combines the two popular fields of machine learning and Python language, and uses two core machine learning algorithms to maximize the advantages of Python language in data analysis. There are 10 chapters in the book.

Chapter 1 explains the Python machine learning ecosystem, and the remaining 9 chapters introduce many algorithms related to machine learning, including various classification algorithms, data visualization technology, recommendation engines, etc., mainly including the application of machine learning in apartments, air tickets, and IPO markets. , news feeds, content promotion, stock markets, images, chatbots and recommendation engines.

"Mastering Python Natural Language Processing" Author: [India] Deepti Chopra, Nisheeth Joshi, Iti Mathur Natural language processing is one of the fields related to human-computer interaction in computational linguistics and artificial intelligence.

This book is a comprehensive study guide for learning natural language processing. It introduces how to use Python to implement various NLP tasks to help readers create projects based on real-life applications.

The book has 10 chapters in total, covering topics such as string operations, statistical language modeling, morphology, part-of-speech tagging, syntax parsing, semantic analysis, sentiment analysis, information retrieval, discourse analysis, and NLP system evaluation.

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

Python Data Science Guide" Author: [India] Gopi Subramanian (Subramanian) More than 60 practical development skills to help you explore Python and its powerful data science capabilities. As a high-level programming language, Python has Its simplicity, readability, and scalability have increasingly become a highly respected language in the field of programming, and it has become one of the first choices of data scientists.

This book introduces the application of Python in data science in detail, including topics such as data exploration, data analysis and mining, machine learning, and large-scale machine learning.

Each chapter provides readers with sufficient mathematical knowledge and code examples to understand algorithm functions at different depths, helping readers better grasp each knowledge point. With its clear structure and complete examples, this book will benefit both newbies and experienced data scientists.

Author of "Writing Web Crawler with Python": [Australia] Richard Lawson (Richard Lawson) This book explains how to use Python to write web crawler programs. The content includes an introduction to web crawlers and three methods of crawling data from pages. , extract data in the cache, use multiple threads and processes for concurrent crawling, how to crawl content in dynamic pages, interact with forms, handle verification code issues in pages, and use Scarpy and Portia for data crawling Fetched, and finally used the data crawling technology introduced in this book to crawl several real websites, aiming to help readers learn and apply the technology introduced in the book.

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

"Bayesian Thinking: A Python Learning Method for Statistical Modeling" Author: [US] Allen B. Downey This book helps those who want to use mathematical tools to solve practical problems. The only requirement may be to understand a little probability knowledge and procedures. design.

The Bayesian method is a common mathematical method that uses probability knowledge to solve uncertainty problems. For a computer professional, you should be familiar with its application in common computers such as machine translation, speech recognition, spam detection, etc. problem areas.

Python Natural Language Processing" Author: [US] Steven Bird, Ewan Klein, Edward Loper Natural Language Processing (NLP) is an important direction in the field of computer science and artificial intelligence.

It studies various theories and methods that enable effective communication in natural language between humans and computers, involving all operations on natural language performed by computers.

"Python Natural Language Processing" is a practical introductory guide to the field of natural language processing, designed to help readers learn how to write programs to analyze written language.

"Python Natural Language Processing" is based on the Python programming language and an open source library called NLTK, a natural language toolkit, but does not require readers to have Python programming experience. The book has 11 chapters in total, arranged in order of difficulty.

Chapters 1 to 3 introduce the basics of language processing and describe how to use small Python programs to analyze text information of interest. 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 annotation, classification, and information extraction. Chapters 8 to 10 introduce sentence analysis, syntactic structure identification and sentence meaning expression methods. Chapter 11 describes how to effectively manage language data.

The postscript briefly discusses the past and future of the NLP field. This book is highly practical and includes hundreds of practical examples and graded exercises.

It can be used for self-study by readers, and can also be used as a textbook for natural language processing or computational linguistics courses. It can also be used as a supplementary reading for courses such as artificial intelligence, text mining, and corpus linguistics.

Python Data Analysis" Author: [Indonesia] Ivan IdrisPython is a multi-paradigm programming language that is suitable for both object-oriented application development and functional design patterns.

Python has become an ideal programming language for data scientists for data analysis, visualization, and machine learning. It can help you quickly improve your work efficiency.

This book will lead novices to become familiar with all aspects of Python data analysis-related fields, from data retrieval, cleaning, operation, visualization, storage to advanced analysis and modeling.

At the same time, this book focuses on explaining a series of open source Python modules, such as NumPy, SciPy, matplotlib, pandas, IPython, Cython, scikit-learn, and NLTK.

Additionally, this book covers topics such as data visualization, signal processing, time series analysis, databases, predictive analytics, and machine learning. By reading this book, you will become a master of data analysis.

Python teaching materials suitable for children to learn

Link: Extraction code: utio Python, which is popular all over the world, ranks first in the IEEE2017 programming language rankings. It has a relaxed language environment and an excellent entry experience. It is the most suitable programming language for beginners. Python is more than just one of the most popular computer languages in the world.

It is also the basic language for artificial intelligence and big data development. Python Programming Winter Camp - Entry-Level, Bund Education collaborates with Professor Payne, the author of Amazon's most popular children's programming book.

Leading children into the real world of programming, adapted to children aged 8-15 with no basic knowledge, 8 video file recorded lessons + 8-day intensive training class.

If I want to learn python by myself, what are the recommended learning methods?

Life is short, I choose Python!

It can be said that this is an era in which everyone should know Python. Financial and administrative personnel can operate Excel through Python; new media operations use crawlers to collect articles and make data analysis reports; write payment reminder systems; in addition, they can also write games (Develop jigsaw puzzles, airplane battles); use Python to grab train tickets and low-cost air tickets, etc.

For those who have no basic knowledge, if you want to learn programming, many people will definitely recommend that you start with python. The reason is simple, because it is simple enough and easy to use.

Even primary school students are starting to learn Python courses now. Python has a wide range of uses and can be called almost omnipotent. It is gradually widely used in back-end development, front-end development, crawlers, financial quantitative analysis, automated operation and maintenance, automated operation and maintenance, and big data. In fields such as Python, I believe its popularity will continue to heat up.

Of course, there are many ways to learn Python, some free and some paid. There are also many online Python learning resources, such as books, documents, videos, audios, etc. If you have certain learning ability and time management ability, You can learn and get started by yourself through free online video resources. If you are not so self-disciplined and can arrange yourself reasonably, then it is possible.

. . emm....just learn from the teacher honestly! I will also summarize some tutorials from time to time. Friends who need it can follow it!

There are also many tutorials on the Internet, and they are a mixed bag. It is best to look for tutorials in the classroom. This is more structured, logical, and friendly to novices. It is a better self-study method. Of course, if you add some Guidance from a technical expert would be perfect.

The following content is suitable for those who have a deep interest in Python and want to conduct in-depth research in the field of data analysis and artificial intelligence.

The Python learning route can be used as the following reference: 2020 Python Artificial Intelligence + Data Analysis Course Outline: Phase 1 - Python Data Science Python Basic Syntax Introduction and Environment Installation, Basic Syntax and Data Types, Control Statements, Errors and Exceptions, Error Handling Methods, Exceptions Processing methods, commonly used built-in functions, function creation and use, Python advanced features, advanced functions, Python modules, PythonIO operations, date and time, classes and object-oriented, Python connection to database Python data cleaning digital Python module Numpy, data analysis tool Pandas, Pandas basic operations, Pandas advanced operations Python data visualization data visualization basics, MLlib (RDD-Base API) machine learning, MatPlotlib drawing advanced, advanced drawing tools phase 2 - business data visualization Excel business analysis Excel basic skills, Excel formula functions, Chart visualization, human & financial analysis cases, business data analysis methods, business data analysis reports Mysql database Mysql basic operations (1), Mysql basic operations (2), Mysql intermediate operations, Mysql advanced operations, e-commerce data processing cases PowerBI elementary business Intelligent application (PowerQuery), primary business intelligence application (PowerPivot), primary business intelligence application case, stored procedure, PowerBI Desktop case, PowerBI Query case Statistics basic calculus, linear algebra basics, statistical basics TableauTableau basic operations, Tableau drawing, Tableau Data analysis, Tableau traffic analysis SPSS customer portrait, customer value model, neural network, decision tree, time series Phase 3 - Python machine learning Python statistical analysis data preparation, single linear regression, multiple linear regression, general logistic regression, logistic regression and Revised Python machine learning basics introduction to machine learning, KNN handouts, model evaluation methods, model optimization methods, Kmeans, DBSCAN, decision tree algorithm practical PythonMachine learning intermediate linear regression, model optimization method, logistic regression, naive Bayes, association rules, collaborative filtering, recommendation system case Python machine learning advanced ensemble algorithm-random forest, ensemble algorithm-AdaBoost, data processing and feature engineering, SVM, Neural Network, XGBoost Phase 4 - Practical Project E-commerce Market Data Mining Project Practical Project Background & Business Logic, Designated Analysis Strategy, Method Implementation and Results, Marketing Activity Design and Results Evaluation, Writing Data Analysis Report Financial Risk Credit Assessment Project Practical Project Background & business logic, modeling preparation, data cleaning, model training, model evaluation, model deployment and update The fifth stage - data collection crawler library analysis, data analysis, dynamic web page extraction, verification code, IP pool, multi-threaded crawler, Anti-climbing countermeasures, scrapy framework phase six - outdoor development training for corporate teams, corporate cooperation project courses, management courses, communication and expression training, and professional quality courses. The above is all the content of the zero-based Python learning route. I hope it will be helpful to everyone's learning. helped.

Finally, some learning suggestions: before studying, set a goal plan for yourself and cultivate your interest in programming. During the learning process, you must touch the code and learn to take notes, but you don’t need to deliberately remember the codes and understand the code comparison. Remembering the code is more important.

Learn the ability to use search engines and learn to solve problems by yourself. In addition to these, you should read more technical columns of big cows, understand your current situation through comparison with big cows, and make timely adjustments and changes. Learning to program is a long-term process.

All friends must have a long-term plan of their own, and break the long-term plan into segmented goals. After the goals are achieved, give yourself a certain amount of motivation. In one word, just cheer and that's it.

What should I learn about python?

Python is a glue language, so you have to make choices when learning it. For those who need to use python for data analysis, we need to at least learn pandas in python. There are two keywords in this sentence, at least and pandas. Let’s talk about pandas first.

What is pandas? Baidu Encyclopedia explains this: "pandas is a tool based on NumPy, which was created to solve data analysis tasks." Although a new term "numpy" has appeared, this does not affect Our understanding of this sentence: pandas is a tool used for data analysis in Python.

Seeing this, you may have questions. Pandas is used for data analysis, so isn’t Python used for data analysis?

The answer is YES. Python is a programming language. It is not specifically used to analyze data. The tools specifically used to analyze data/statistics are tools such as SPSS.

Therefore, pandas is to python just like the data analysis function in Excel is to Excel. Do you think Excel is used for data analysis?

No, Excel is obviously office software and can be used by all walks of life. It is not exclusive to data analysis, right? Now that the second keyword pandas has been mentioned, let’s go back and look at the first keyword, at least.

This is easy to understand. When we learn to use Excel for data analysis, we can use it for most purposes after learning the data analysis function, but this does not affect our continued learning of graphing, functions, etc.

That’s what it means. The pandas package is only basic. If you want to go deeper, it is necessary to learn and learn again.