Python Big Data Analysis: From Beginner to Mastery

Name: Python Big Data Analysis: From Beginner to Mastery
ISBN: 9787301313558

Lan YijiePeking University Press · ChinaAug 2025

3.0

Word count: 434 words
Chapters: 20
ISBN: 9787301313558

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Summary

This book explains how to integrate and use Python on data platforms, focusing on Python's role in data analysis. It is divided into three parts. Part 1 (Chapters 1–3) covers setting up the development environment and importing test data. Part 2 (Chapters 4–12) covers Python operations on HDFS, Hive, Pig, HBase, and Spark, mainly explaining commonly used APIs. Part 3 (Chapters 13–16) builds on the earlier chapters to introduce data analysis, mining, and visualization. The book not only explains how Python is applied on big data platforms but also discusses big data platform management and operations throughout, making it equally suitable for readers who want to learn platform-related knowledge.

Editor's review

The book has a clear focus: it neither teaches Python syntax alone nor discusses big data concepts in general terms, but places Python within operational scenarios involving big data components such as HDFS, Hive, Pig, HBase, and Spark. Structurally, it first sets up the environment and imports test data, then explains commonly used APIs chapter by chapter, and finally moves on to data analysis, mining, and visualization, forming a complete chain from platform operations to presenting results. What is particularly valuable is that explanations of big data platform management and operations run throughout the book, so while learning Python calls, readers also understand the underlying platform mechanisms, which is very helpful for troubleshooting in real work. The book involves many components and has a high information density, so it is suitable for hands-on verification in a lab environment while reading. For readers who already know some Python and want to enter the field of big data analysis, it helps build an overall framework of "language tools + platform components + analysis workflow"; for those mainly doing single-machine data analysis, it also offers a way to understand the differences in distributed environments.

Who it's for

A good fit for

Developers who know basic Python syntax and want to enter the field of big data analysis
Engineers who need to use Python for data processing on big data platforms
Technical staff who want to learn common API operations for components such as HDFS, Hive, and Spark
University students in data-related majors who need to do coursework or project exercises

Not a good fit for

Complete beginners with no programming background who have not yet been exposed to Python
Readers who only want to learn data analysis methodology and do not intend to operate platforms hands-on
Senior engineers seeking in-depth analysis of the principles of a particular component rather than API usage explanations

Reading tips

First set up the development environment and import test data according to Part 1, so that examples in later chapters can be reproduced.
When reading the component operation chapters, execute on the platform while reading the API explanations, and focus on recording parameter differences.
Since there are many components, you can first read in depth the parts needed for your work and skim the rest to build an impression.
After finishing, use the analysis, mining, and visualization content in Part 3 to run through the complete workflow and test your mastery.

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
第一章为什么选择用Python
第二章大数据开发环境的搭建
第三章构建分析数据
第四章 Python对Hadoop的操作
第五章 Python 对Hive的操作
第六章 Python对HBase的操作
第七章 Python 集成到Pig
第八章 PySpark Shell应用
第九章 PySpark 对RDD操作
第十章 PySpark对DataFrame的操作
第十一章 PySpark对Streaming的操作
第十二章 PySpark SQL
第十三章分析方法及构架的说明
第十四章集成分析
第十五章数据挖掘
第十六章数据可视化
附录

FAQ

Can I read this book without a big data platform background?

Yes, but be prepared to fill in some gaps. The book starts with setting up the development environment and importing test data, and includes explanations of platform operations throughout, so readers with no platform experience can follow along from the start. However, since many components are involved, it is advisable to first understand basic concepts such as HDFS and Hive before moving on to the API operation chapters for smoother comprehension.

Is this book more theoretical or more hands-on?

Overall it is more hands-on. Part 2 focuses on explanations of commonly used Python APIs for HDFS, Hive, Pig, HBase, and Spark, and Part 3 builds on that to cover analysis, mining, and visualization. It is recommended to read it in a runnable lab environment and execute the examples while reading; simply reading through tends to leave you at the level of memorizing interfaces.

There are so many components involved—do I need to learn them all?

You do not need to master all of them. The component chapters are relatively independent, so you can first read through to understand the overall picture and then study in depth the parts needed for actual projects. Explanations of platform management and operations run throughout the book; paying attention to this thread while reading helps you understand where each component fits in the overall workflow.

After finishing, can I independently complete a big data analysis project?

The book covers the complete chain from environment setup and platform component operations to analysis, mining, and visualization, so after finishing you can build an overall framework and gain hands-on ability. However, real projects also involve engineering issues such as data quality, scheduling, and performance tuning. It is recommended to use the book's workflow as a starting point and run through the complete process from importing data to outputting results in a practice environment.

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About the author

Lan Yijie Author · Chinese

Lan Yijie is a senior software engineer and project manager who has long focused on and practiced the integrated use of data analysis and deep learning technologies. Publicly available information is limited, and details of his professional career and personal background have not been disclosed in depth, but his published works show that his writing is oriented toward practical application. He has spent years in development work involving Python-based data operations, master data projects, and big data analysis projects, with technical experience spanning both small data and big data. His representative works include Starting from Scratch: Data Analysis with Excel and Python, Python Big Data Analysis from Beginner to Mastery, and AI Prompt Engineering in Practice: Learning to Apply Large Language Models with Prompt Engineering from Scratch, most of which fall under programming design and general computing. These works follow a path from introductory to hands-on material, emphasizing the combination of tools and methods with real-world scenarios, and are suitable for readers who wish to systematically master data processing and prompt engineering skills.