Python Data Analysis, Mining and Visualization: From Beginner to Mastery

Name: Python Data Analysis, Mining and Visualization: From Beginner to Mastery
ISBN: 9787301347690

熊熙 / 张雪莲Peking University Press · ChinaJun 2025

3.4

Word count: 120K words
Chapters: 95
ISBN: 9787301347690

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Summary

The book is divided into four parts. Part 1, Foundations, introduces the basic concepts of data analysis and mining along with the fundamentals of data analysis in Python. Part 2, Data Analysis, covers common data analysis methods. Part 3, Data Mining, covers common data mining methods. Part 4, Practical Applications, presents two complete data analysis and mining case studies.

Editor's review

This book unfolds progressively in four sections—Foundations, Data Analysis, Data Mining, and Practical Application—making it well suited as a complete learning path. Its value lies not in listing scattered tips but in connecting concepts, methods, and cases into one line: the opening lays out basic concepts and Python foundations for data analysis, the middle sections separately develop common data analysis methods and data mining methods, and the final section grounds earlier material in two complete cases that walk through real workflows. For beginners, this structure reduces the disconnect of “finishing the syntax but not knowing how to apply it”; for those with some background, the case section can help organize an overall approach to analysis and mining. When reading, avoid skipping straight to the cases—the correspondence between methods and cases is the book’s main highlight. Note that the book covers many methods, and a single read-through is unlikely to yield true mastery; hands-on practice is best.

Who it's for

A good fit for

Beginners new to data analysis who want to systematically build a Python analysis workflow
Students who need to learn data analysis and data mining methods in an integrated way
Readers who want to understand how analysis and mining are applied through complete cases
Readers with some Python foundation who want to fill out their toolkit of data analysis methods

Not a good fit for

Readers seeking deep theoretical or mathematical derivations of specific algorithms
Practitioners already fluent in the full data analysis and mining workflow who want advanced topics
Readers unwilling to practice hands-on and only wanting to skim conclusions

Reading tips

Follow the four sections in order: first solidify basic concepts and Python analysis foundations, then move to methods and cases.
When reading the data analysis and data mining sections, run the code yourself and adjust parameters to observe results.
Before studying the practical cases, outline your own analysis approach, then compare it with the book’s workflow.
After learning each type of method, reproduce it once with your own small dataset to check true 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
内容简介
前言
第1篇基础入门篇
第1章从零开始：初识数据分析与挖掘
1.1 什么是数据分析
1.2 什么是数据挖掘
1.3 数据分析与挖掘的应用领域
1.4 用Python进行数据分析与挖掘
1.5 本章小结
1.6 思考与练习
第2章 Python数据分析基础
2.1 搭建Python开发环境
2.2 Python基础
2.3 本章小结
2.4 思考与练习
第3章 Python数据分析相关库应用
3.1 NumPy
3.2 SciPy
3.3 Pandas
3.4 Scikit-learn
3.5 其他常用模块
3.6 本章小结
3.7 思考与练习
第2篇数据分析篇
第4章数据的预处理
4.1 数据获取
4.2 文件与数据存储
4.3 数据清洗
4.4 本章小结
4.5 思考与练习
第5章数据的分析方法
5.1 分布分析
5.2 对比分析
5.3 统计量分析
5.4 相关性分析
5.5 帕累托分析
5.6 正态分布分析
5.7 本章小结
5.8 思考与练习
第6章数据可视化工具的应用
6.1 数据可视化工具——Matplotlib
6.2 数据可视化工具——Seaborn
6.3 本章小结
6.4 思考与练习
第3篇数据挖掘篇
第7章数据挖掘之线性回归
7.1 线性回归概述
7.2 一元线性回归
7.3 多元线性回归
7.4 线性回归模型的评估与检验
7.5 本章小结
7.6 思考与练习
第8章数据挖掘之分类模型
8.1 逻辑回归模型
8.2 决策树和随机森林算法
8.3 KNN算法
8.4 本章小结
8.5 思考与练习
第9章数据挖掘之关联分析
9.1 关联分析概述
9.2 Apriori关联分析算法
9.3 FP-growth关联分析算法
9.4 本章小结
9.5 思考与练习
第10章数据挖掘之聚类分析
10.1 聚类分析概述
10.2 质心聚类——K-Means算法
10.3 密度聚类——DBSCAN算法
10.4 层次聚类——AGNES算法
10.5 本章小结
10.6 思考与练习
第4篇实战应用篇
第11章实战案例：房价评估数据分析与挖掘
11.1 加载数据集
11.2 数据分析
11.3 数据可视化
11.4 数据预处理
11.5 拆分数据集
11.6 建立线性回归模型
11.7 建立决策树模型
11.8 对比分析构建的线性回归和决策树模型
11.9 本章小结
第12章实战案例：电信客户流失数据分析与挖掘
12.1 案例背景
12.2 加载数据
12.3 数据准备
12.4 数据清洗
12.5 数据处理
12.6 数据可视化
12.7 特征工程
12.8 数据建模
12.9 训练模型
12.10 模型的评估
12.11 本章小结

FAQ

Can I read this book without any Python background?

Yes, but you need to build foundations alongside it. Section 1 covers Python foundations for data analysis and can help you get started; however, the methods and cases later move faster. When reading Section 1, write code hands-on and practice basic syntax and common operations until comfortable before entering the data analysis section, or you may get stuck in the case section.

What is the difference between the data analysis and data mining parts?

According to the book’s structure, Section 2 focuses on common data analysis methods and Section 3 on common data mining methods; the two differ in goals and methods. As you read, note what problems the methods in each section solve, then see through the two complete cases in Section 4 how they connect within a single workflow.

How should I use the two practical cases?

Don’t just read—practice. First outline your own approach based on the case goals, then compare it with the book’s analysis and mining steps, focusing on method selection, workflow organization, and result interpretation. Finally, try reproducing the case with your own data to better appreciate how the methods from earlier sections map onto real tasks.

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About the author

熊熙 Author

Xiong Xi is an author about whom limited public information is available; the main identity that can be confirmed at present is related to writing computer books. The representative work, Python Data Analysis, Mining and Visualization: From Beginner to Mastery, falls under the general computing category. Judging from the title, the book is aimed at beginners and systematically explains how to use Python for data processing, data mining and visualization, seeking to help readers progress from the basics toward mastery. According to the available information, Xiong Xi's nationality, dates of birth and death, educational background and professional experience are not clearly recorded, so no further inferences should be made about the author's personal life. What can be confirmed is that Xiong Xi focuses on data analysis and programming education; the work reflects a technical writing approach that emphasizes practice and proceeds from the simple to the deeper, making it suitable for readers who wish to use Python to grasp the basic workflow of data analysis and methods of visual presentation.

张雪莲 Author

Zhang Xuelian is an author whose publicly available information is limited. What can be confirmed is that she writes computer books; the library holds one work by her, Python Data Analysis, Mining and Visualization from Beginner to Mastery. Judging from the title and its general classification under "computer science," the book is aimed at readers who want to use Python for data processing and graphical presentation, and it covers a fairly complete introductory chain ranging from data analysis and data mining to visualization. This marks it as a typical practice-oriented technical work. Little further public information is available about the author's writing style or personal background, but the choice of topic suggests that she focuses on connecting a programming language with real data-handling tasks, helping beginners build a basic understanding of the workflow from reading data and analyzing features to presenting results visually. For readers seeking a systematic introduction to Python for data work, such a book offers clear reference value.