Python Big Data Analysis

Name: Python Big Data Analysis
ISBN: 9787113191825

吴道君 / 沈阳 / 陈素霞China Railway Publishing House · ChinaFeb 2026

3.4

Word count: 58K words
Chapters: 62
ISBN: 9787113191825

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Summary

This book is one of the "14th Five-Year Plan" series of integrated, application-oriented textbooks for talent development, offering a comprehensive introduction to Python data analysis. It covers an overview of Python data analysis, NumPy numerical computing, Matplotlib data visualization, Pandas data analysis, data preprocessing, and Sklearn machine learning. Aimed at developing students' programming and data analysis skills, the book emphasizes practical technical application.

Substantive in content, well structured, and highly practical, it is distinctly oriented toward building applied skills. After completing the book, readers will have acquired basic data analysis capabilities.

It is suitable as a textbook for courses in artificial intelligence, data science and big data, and computer-related majors at regular higher education institutions, and can also serve as a technical reference for practitioners in related fields.

Editor's review

This book follows the complete pipeline of Python data analysis, starting with an overview and then covering NumPy numerical computing, Matplotlib visualization, Pandas data processing, data preprocessing, and Sklearn machine learning. The structure follows the order of technical application, helping readers build a holistic understanding from data to model. As an integrated, new-form textbook, it emphasizes developing programming skills and data analysis skills in tandem, focusing on practical application rather than theoretical exposition; upon completion, readers will have basic hands-on data analysis capabilities. For university students in related majors, it works well as a main course textbook, progressing chapter by chapter with hands-on exercises; for practitioners, it serves as a technical reference to consult chapters on numerical computing, visualization, or preprocessing as needed. Readers are advised to write code while learning, using a small real-world dataset throughout each chapter's exercises rather than reading without practicing; before entering the machine learning section, solidify Pandas and data preprocessing first so that subsequent modeling goes more smoothly.

Who it's for

A good fit for

Students in artificial intelligence, data science, and computer-related majors at regular higher education institutions, using it as a course textbook
Learners with Python basics who want a systematic introduction to data analysis, studying chapter by chapter
Working technical professionals needing to strengthen NumPy, Pandas, and visualization skills, consulting it as a reference
Readers preparing to enter the applied machine learning stage who want to first build a solid foundation in data processing

Not a good fit for

Readers with no programming experience at all; the book assumes basic Python syntax skills
Readers seeking in-depth machine learning algorithm principles and derivations; the book focuses on technical application
Readers looking for big data platforms and distributed computing content; the book focuses on single-machine analysis tools

Reading tips

Proceed in the order of overview, NumPy, Matplotlib, Pandas, preprocessing, and Sklearn, without skipping chapters
Use a small dataset for hands-on coding in each chapter, rewriting the sample code into your own practice version
Before studying machine learning, review Pandas and data preprocessing to ensure proficiency in data cleaning and transformation
Keep a code notebook recording common functions and error handling for future reference and reuse

About the publisher

China Railway Publishing House China · Founded in 1951

China Railway Publishing House has long centered on railway professional publishing while also covering rail transit, industrial technology, programming and design, finance and economics, and exam preparation, with about 194 relevant titles in the collection. Representative books include the Automated Operation (FAO) System for Urban Rail Transit, Design Innovation and Practice for Urban Rail Transit Vehicle Depots, Analysis and Application Management of the Level-One Maintenance Inspection Robot System for Electric Multiple Units, as well as engineering practice titles such as the Engineering Summary of the Guangdong Section of the Ganzhou–Shenzhen Railway and Safety Risk Control Technology and Typical Cases of Shield Tunneling in Beijing Urban Rail Transit Construction. Beyond technical monographs, the publisher also offers practical titles such as Python Programming Fundamentals and tax and accounting treatment for real estate development enterprises, with authors including He Peng, Tang Tao, Ding Shukui, and Sun Changjun. Based on public information, its publishing strength lies in drawing on line engineering and operational practice while balancing professional textbooks and general-audience titles; more complete details of its publishing structure are limited in public sources.

版权信息
内容简介
前言
第1章 Python数据分析概述
1.1 数据分析的概念、流程和应用
1.2 数据分析工具
1.3 Python数据分析环境
小结
习题
实验
第2章 NumPy数值计算
2.1 NumPy多维数组
2.2 数组操作
2.3 数组索引与切片
2.4 数组的运算
2.5 线性代数
2.6 数组的存取
2.7 视图和副本
2.8 随机漫步
小结
习题
实验
第3章 Matplotlib数据可视化
3.1 折线图
3.2 图表属性设置
3.3 其他图形
3.4 子图
3.5 绘制三维图形
小结
习题
实验
第4章 Pandas数据分析
4.1 Pandas数据结构
4.2 DataFrame基本功能
4.3 读取外部数据
4.4 数据帧的列操作和行操作
4.5 高级索引
4.6 Pandas数据运算
4.7 统计函数
4.8 分组与聚合
4.9 透视表与交叉表
小结
习题
实验
第5章数据预处理
5.1 数据清洗
5.2 合并连接与重塑
5.3 数据变换
小结
习题
实验
第6章 Sklearn机器学习
6.1 术语
6.2 Sklearn
6.3 降维
6.4 回归
6.5 分类
6.6 聚类
小结
习题
实验
参考文献

FAQ

Can I learn this book without any Python foundation?

It is not really recommended to start directly. The book moves from an overview of data analysis straight into the use of libraries such as NumPy, assuming readers already grasp basic Python syntax, data types, and simple programming. Complete beginners should first study introductory Python content before moving to this book; otherwise they may get stuck on the code examples.

What level can I reach after finishing this book?

According to the material, after completing the book readers will have basic data analysis capabilities, including using NumPy for numerical computing, Matplotlib for visualization, Pandas for data processing, completing data preprocessing, and using Sklearn for basic machine learning applications. This is at the introductory-to-applied level and still requires project practice for further consolidation.

Does the machine learning section go deep?

The book is positioned as an application-oriented textbook; the machine learning section focuses on applying the Sklearn tool, emphasizing the ability to call relevant methods to complete analysis tasks rather than delving into algorithm derivations and mathematical proofs. Readers who want to study algorithm principles will need a separate dedicated machine learning theory book.

Is it suitable for self-study?

It is suitable for self-learners with some Python foundation. The book is clearly structured, application-focused, and its chapter order follows a logical learning progression. For self-study, it is best to type out the code on a computer, complete each chapter's exercises before moving on, and prepare your own dataset to use throughout the exercises for better results.

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About the author

吴道君 Author

Wu Daojun: publicly available information about this author is currently limited, and details such as his specific biography, nationality, and dates of birth and death remain unclear. Judging from the two works held in the library, Wu Daojun's writing is mainly oriented toward computer science teaching and technical application. His representative works are the Java Programming Tutorial (Third Edition) and Python Big Data Analysis. The former is an introductory textbook for programming courses, emphasizing grammar fundamentals and a step-by-step approach to programming thinking; the latter focuses on data analysis practice, using Python tools to handle big-data scenarios. The two works correspond respectively to the general computer science and examination categories, indicating that their content serves both classroom teaching and skills-test preparation needs. Overall, Wu Daojun's writing style tends to be practical and instruction-oriented, with clear and accessible language that helps beginners and exam-preparation readers master knowledge systematically, offering certain reference value for the relevant readership.

沈阳 Author

Shen Yang: publicly available information is limited, and the author's identity and background cannot presently be verified. Judging from the four works held in the collection, the writing spans several fields, including language studies, social culture and practical technology: there is a general-reader language title, Fifteen Lectures on Chinese and Chinese Studies, to which the author contributed; a work on contemporary issues, The Age of Dialogue: Forging a Strong Nation through New Quality Productive Forces; and practical technical content such as Python Big Data Analysis. The works are commonly categorized under literary appreciation, language and writing, social culture and examinations, suggesting an approach that combines academic popularization with practical application and addresses a fairly broad readership.

陈素霞 Author

Only limited public information is currently available about Chen Suxia, whose signed work is Python Big Data Analysis. This book indicates that Chen Suxia focuses on the practical application of programming languages in data analysis, choosing the intersection of Python as a tool and big data processing methods. The book is classified under examinations in the library, suggesting that it may target exam preparation, course study, or skills certification rather than pure academic research or popular reading for a general audience. Works of this kind typically emphasize step-by-step operations, case demonstrations, and well-organized knowledge points, helping readers learn and practice while gradually mastering the basic workflow of data analysis. Given the available information, Chen Suxia's personal background, research areas, and writing experience remain unclear, and the actual impact of the work awaits further information.