Python Data Analysis and Big Data Processing: From Beginner to Mastery

Name: Python Data Analysis and Big Data Processing: From Beginner to Mastery
ISBN: 9787301307656

朱春旭Peking University Press · ChinaAug 2025

3.2

Word count: 161K words
Chapters: 30
ISBN: 9787301307656

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Summary

This book is divided into five parts and 20 chapters. It starts with Python programming, then moves on to data analysis and visualization tools, and then to big data storage and parallel computing frameworks, progressing step by step from basic to advanced. It then uses three project examples to apply and summarize the content of the first three parts, guiding readers until they master the Python big data development workflow and programming skills.

Editor's review

This book's structure is well suited to readers who want a systematic introduction to Python data analysis and big data development. The book is divided into five parts and twenty chapters: it first covers the fundamentals of Python programming, then moves on to data analysis and visualization tools, and then to big data storage and parallel computing frameworks, finally tying together what was learned earlier with three project examples. This step-by-step approach avoids the fragmentation that comes from simply piling up tools and frameworks, allowing readers to progress along a complete technical path. The emphasis on programming skills and the development workflow makes the book more focused on building practical ability than comparable introductory books, rather than merely listing APIs. For self-learners who need to build a knowledge framework from scratch and ultimately hope to independently complete a big data development workflow, the book's chapter arrangement provides a clear learning route. When reading, it is advisable to focus on the project examples and test your understanding of the first three parts by reproducing them hands-on. If you encounter environment issues such as framework configuration, consult the relevant tool documentation first, then return to the book to compare with the workflow.

Who it's for

A good fit for

Readers with no background or only limited programming experience who want to systematically learn Python data analysis
Learners who need to move from data analysis to big data storage and parallel computing frameworks
University students in related majors using it as course material or for self-study
Career changers who hope to master the complete big data development workflow through project examples

Not a good fit for

Practitioners with extensive big data development experience seeking advanced, in-depth content
Readers who only want to quickly look up how to use a specific tool and do not want systematic learning
Readers with no interest in programming who only need to understand data analysis concepts

Reading tips

Proceed in the order of the five parts; after finishing each of the first three parts, reproduce the book's examples hands-on before moving to the next part.
When reading the project examples section, try independently first; when stuck, compare with the book's workflow and note the differences and reasons.
Prepare a separate runtime environment for the big data frameworks section, configuring while reading, to avoid reading without practicing.
After each chapter, summarize in your own words where that chapter's tools fit in the overall development workflow.

About the publisher

Peking University Press China · Founded in 1979

Peking University Press is a comprehensive university press whose publishing areas cover classical literature, popular science, social culture, world history, Western philosophy, and culture. Its catalogue contains 6,031 titles, with representative books including A Dream of Red Mansions, Relativity: The Special and General Theory (15th Edition) (with Einstein's classic papers on unified field theory and other topics), Elements, Conics, The Aristocratic Families of the Eastern Jin, The Philosophy of Mythology (Volume I), The Philosophy of Mythology (Volume II), and The Origin of Continents and Oceans. Representative authors include Cao Xueqin, Zhou Ruchang, Albert Einstein, Euclid, Apollonius, Tian Yuqing, Schelling, and Xian Gang. The press has developed distinctive strengths in classical Chinese literature and in the translation of Western philosophical classics, while also covering scientific classics and social and cultural studies. As public information is limited, this description is based only on the books in the catalogue and offers no further extrapolation.

版权信息
内容提要
前言
第1篇 Python程序设计
第1章 Python入门
第2章 Python基础
第3章数据类型与流程控制
第4章函数、模块、包
第5章面向对象的程序设计
第6章高级主题
第2篇数据采集与数据清洗
第7章网络数据采集
第8章数据清洗
第3篇数据分析与可视化
第9章 NumPy数值计算
第10章 Matplotlib可视化
第11章 Pandas统计分析
第12章 Seaborn可视化
第4篇大数据存储与快速分析篇
第13章 Hadoop数据存储与基本操作
第14章 Spark入门
第15章 Spark RDD编程
第16章 Spark SQL编程
第17章 Spark流式计算编程
第5篇项目实战篇
第18章分析电商网站销售数据
第19章分析旅游网站数据
第20章分析在售二手房数据
附录 Python常见面试题精选
主要参考文献

FAQ

Can I read this book directly without any programming background?

Yes, but it requires considerable time. The book starts with Python programming, and the first part is prepared for beginners. It is recommended not to skip the programming fundamentals; first make sure you can independently write simple programs before moving on to data analysis and big data, otherwise it will be easy to fall behind in the later framework content.

What runtime environment do the project examples in the book require?

The project examples involve data analysis tools and big data storage and parallel computing frameworks, which usually require configuring the corresponding software on a local machine or in a virtual environment. It is recommended to set up the environment in advance according to the book's instructions and allow time for debugging. For specific dependency versions, follow what is stated in the book; if you encounter environment issues, troubleshoot with reference to the tools' official documentation.

What level can I reach after finishing the book?

After completing the book's learning path and practicing hands-on, you should be able to understand the basic workflow of Python data analysis and big data development and use the relevant tools to complete data processing, visualization, and parallel computing tasks. However, mastery still requires repeated practice in real projects afterward; this book provides a systematic introductory framework and project training.

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About the author

朱春旭 Author · China

Zhu Chunxu is a Chinese author of computer books. Publicly available information about the author is limited: neither birth and death years nor place of origin or educational background have been clearly recorded, and the verifiable information currently centers on a single work. That representative work is Python Data Analysis and Big Data Processing from Beginner to Master, classified under general computing. The book is aimed at readers new to data analysis and big data processing, and seeks to connect programming fundamentals and data-handling approaches with practical application. Judging by the choice of subject, the author focuses on the practical use of the Python language in the data field, and the writing follows a step-by-step tutorial style that moves from the basics to more advanced material, making it suitable for beginners who want to get started with data analysis systematically. Since only this one work is held in the collection, it is difficult to say more about the author's full body of writing or personal history; readers may take this book as a direct entry point for understanding the author's technical interests.