A Practical Guide to the Full Workflow of Python Data Analysis

Name: A Practical Guide to the Full Workflow of Python Data Analysis
ISBN: 9787301289495

尚涛Peking University Press · ChinaAug 2025

3.4

Word count: 270 words
Chapters: 19
ISBN: 9787301289495

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Summary

A Complete Practical Guide to the Python Data Analysis Workflow centers on the standard process of the data analysis methodology, introducing the commonly used Python toolkits, including the scientific computing library NumPy, the data analysis library Pandas, the data mining library Scikit-Learn, and the data visualization libraries Matplotlib and Seaborn. Starting from real business applications of data analysis and mining, it explains authentic cases from the internet, finance, and retail industries, such as customer segmentation, precision product marketing, house price prediction, and feature dimensionality reduction, guiding readers through the entire Python data analysis process in an accessible, step-by-step manner.

Concise and focused, with abundant cases, A Complete Practical Guide to the Python Data Analysis Workflow is suitable for professionals engaged in data analysis, data mining, and machine learning in enterprises, as well as students and teachers at secondary and higher education institutions who wish to pursue careers in data analysis and mining, and all others interested in the field of data analysis and mining technology.

Editor's review

This book takes the standard data analysis workflow as its main thread, linking tools such as NumPy, Pandas, Scikit-Learn, Matplotlib, and Seaborn into an actionable practical path, and uses cases such as customer segmentation, precision marketing, house price prediction, and feature dimensionality reduction to demonstrate the process from data to conclusions. Its highlight is the "full workflow" mindset: rather than teaching only how to use a particular library, it emphasizes the connections among data cleaning, feature processing, modeling, and visualization, making it suitable for readers who need to turn scattered syntax into complete project capability. While reading, it is recommended to reproduce the cases locally as you go, paying particular attention to data preprocessing and feature engineering, as these most affect the results. The book is aimed at learners with practical needs and is operationally oriented; those with no programming experience at all may need to first build up Python fundamentals. For college students and career changers, it can serve as a reference for project practice and workflow organization.

Who it's for

A good fit for

Junior and mid-level practitioners engaged in data analysis, data mining, or machine learning in enterprises
College students who want to systematically work through the full data analysis workflow and need project cases for practice
Self-taught readers preparing to move into data analysis who already have Python fundamentals
Teachers who teach data analysis courses and need case materials and workflow frameworks

Not a good fit for

Readers with no programming foundation at all, who need to first build up Python syntax and basic environment setup
Advanced researchers seeking deep learning, large models, or the principles of cutting-edge algorithms
Readers who only want to understand statistical analysis theory and do not intend to write code

Reading tips

Set up the local environment in chapter order and reproduce each case one by one; do not just read the code without running it
After finishing each case, redo it with your own data in place of the original data to test how well you have mastered the workflow
Focus on organizing the data cleaning and feature engineering steps into a reusable checklist
Consult the official documentation when encountering library function usage, and treat the book's cases as workflow demonstrations rather than the only way to write them

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
作者简介
第一章数据分析方法
第二章初识Python
第三章 NumPy数组与矩阵
第四章 Pandas数据分析
第五章 Scikit-Learn数据分析
第六章数据可视化
第七章数据导入与导出
第八章数据预处理
第九章数据探索
第十章线性回归分析
第十一章 Logistic回归分析
第十二章决策树
第十三章主成分分析
第十四章聚类分析
第十五章时间序列分析

FAQ

Can I study this book directly with zero programming background?

It is not recommended to start directly. The book revolves around Python toolkits and real cases, assuming that readers can understand basic syntax and set up a runtime environment. If you have no programming experience at all, it is advisable to first learn basic Python syntax, variables, and functions before starting this book; otherwise, you may easily spend your time on environment errors and find it hard to grasp the full-workflow method.

What kind of data environment do the cases in the book require?

The cases cover scenarios such as the internet, finance, and retail, involving tasks such as customer segmentation, precision marketing, house price prediction, and feature dimensionality reduction. When studying, it is best to install Python and the libraries mentioned in the book locally, prepare the corresponding data for the cases, and run them step by step. If data access is limited, you can substitute public data with a similar structure; the key is to work through the process of cleaning, modeling, and visualization.

Is this book more theoretical or more practical?

It is practical overall. It organizes content around the data analysis workflow and explains basic knowledge of the toolkits and case applications rather than systematically deriving algorithm principles. It is suitable as a project workflow guide for learning by doing; if you want to understand the mathematical derivations behind the models in depth, you will also need dedicated machine learning or statistics textbooks.

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About the author

尚涛 Author

Shang Tao is an author about whom limited public information is available. What can be confirmed is that Shang Tao wrote Python Data Analysis: A Complete Step-by-Step Practical Guide, a book catalogued in the programming and design category. Judging from the title and its usual classification, the book is aimed at readers who want to learn the data analysis workflow systematically, and it focuses on the practical use of Python tools in acquiring, organizing, analyzing, and presenting data, making it a hands-on technical work. The author appears to emphasize the completeness of the workflow and the feasibility of each operation rather than abstract theoretical discussion, guiding readers through steps and examples. Because verifiable information is scarce, Shang Tao's education, professional background, and other writings cannot be confirmed, so understanding of the author currently rests mainly on the practical subject matter and positioning of this single work.