Python Data Science Practice
Tags
Summary
Editor's review
Who it's for
A good fit for
- Learners with Python basics who want to systematically work through the entire data science workflow
- University students and junior analysts who need to apply data analysis to business problems
- Professionals who want to understand the connections among data scraping, cleaning, and modeling through cases
- Instructors who need supplementary cases for university data science courses
Not a good fit for
- Complete beginners with no programming background who need to learn Python syntax first
- Readers interested only in machine learning algorithm derivations and not in data workflows
- Practitioners seeking deep industry-specific business knowledge rather than general workflows
Reading tips
- First read the table of contents to clarify the main line of the data science workflow, then reproduce the case code chapter by chapter
- Run and modify the parameters of each chapter to observe changes in data cleaning and modeling results
- Prepare a small business problem and try to go from scraping to modeling using the book's workflow
- Consult official documentation when encountering module usage, rather than only memorizing the book's examples
About the publisher
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.
Contents
- 版权信息
- 序1 最年长的Python学习者
- 序2 Python教学的新模式
- 前言
- 第1章 基于Python的数据科学环境搭建
- 1.1 Python是数据科学“大势所趋”
- 1.2 Anaconda入门——工欲善其事，必先利其器
- 1.3 Jupyter Notebook入门
- 1.4 Markdown单元格的使用
- 1.5 Spyder入门
- 1.6 小结
- 第2章 Python基础
- 2.1 “火锅团购数据”简介
- 2.2 读写数据
- 2.3 Python数据类型与结构
- 2.4 控制流、函数与模块
- 2.5 面向对象编程的基本概念
- 2.6 Numpy简介
- 2.7 小结
- 第3章 Python的数据处理模块
- 3.1 初级篇——相遇Pandas
- 3.2 进阶篇——相识Pandas
- 3.3 小结
- 第4章 Python的绘图模块
- 4.1 为什么需要数据可视化
- 4.2 初级篇——Matplotlib基础
- 4.3 高级篇——Plotly基础
- 4.4 小结
- 第5章 Python的统计建模模块
- 5.1 Statsmodels简介
- 5.2 数据接入
- 5.3 统计模型参数估计
- 5.4 统计假设检验
- 5.5 探索分析
- 5.6 小结
- 第6章 Python的机器学习模块
- 6.1 机器学习的定义
- 6.2 使用scikit-learn
- 6.3 小结
- 第7章 Python的爬虫模块
- 7.1 爬虫的定义
- 7.2 初级篇——单页面静态爬虫
- 7.3 中级篇——多页面静态爬虫
- 7.4 高级篇——爬虫的伪装
- 7.5 终级篇——动态爬虫
- 7.6 爬虫注意事项
- 7.7 小结
- 第8章 Python的文本分析模块
- 8.1 准备：理解文本分析流程
- 8.2 实战
- 8.3 小结
- 第9章 Python的数据库模块
- 9.1 为什么需要数据库
- 9.2 初级篇——SQLAlchemy的基本使用
- 9.3 高级篇
- 9.4 小结
- 第10章 第10章精品案例——火锅团购分析
- 10.1 背景介绍
- 10.2 数据描述
- 10.3 建模分析
- 10.4 小结
FAQ
Can I read this book directly without a Python foundation?
Can the cases in the book be used directly in actual work?
What environment or tools do I need to prepare for reading?
Comments
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About the author
Chang Xiangyu is a Chinese statistics scholar whose main research interests lie in data science and machine learning methods. His publicly available personal information is limited, and the only work confirmed in the library is Python Data Science Practice. Judging from the subject of this book, Chang focuses on the practical side of data science, seeking to combine a statistical perspective with the Python programming tools for readers who wish to handle data hands-on and understand the modeling process. As a writer, he follows the common style of technical books that emphasize operability, with content centered on methods and applications rather than abstract concepts. Because public material is limited, further details about his education, positions, and full body of work cannot be given. His research field and the positioning of his book suggest an interdisciplinary orientation, bringing statistical training into the context of programming practice. For beginners, such a work helps build connections between tool use and statistical thinking, and thus offers some reference value within the programming and design category.
Zeng Zhiyi is an author about whom limited public information is available. He is known only for one work, Python Data Science in Practice, which falls under programming and design. Judging from the subject of this book, his writing centers on the practical application of Python in data science and is aimed at readers who wish to use Python for data processing and analysis, with an emphasis on combining methods with hands-on operation. Since his dates of birth and death, nationality, educational background, and professional experience are not clearly recorded, no further account of his life can be given. Based on the available information, Zeng Zhiyi's writing focuses on the intersection of programming and data science, with attention to tool use and practical work, which serves as the main clue to understanding his creative profile.
Li Chunyan's publicly available information is limited; details such as nationality, dates of birth and death, and professional background have not been clearly recorded. Judging from the two works included in the library, her writing spans the fields of programming design and finance, covering a fairly wide range. Python Data Science Practice addresses the study and application of data science and belongs to the practical technical genre, while Building the Core City of the Bashu Cultural Tourism Corridor: Theory, Foundation and Chengdu Practice focuses on the integration of regional culture and urban development, combining theory with local practice. One work is technical and the other theoretical, suggesting that the author is able to handle both technical tools and issues of culture and urban development. Because more biographical material is lacking, her writing style and academic trajectory cannot yet be further summarized; readers may learn about her areas of interest through the works mentioned above.
Cheng Qian is a Chinese-language book author about whom little public information is available. The only work currently confirmed is Python Data Science Practice, with one title recorded in the catalog under the common category of programming and design. Judging from the subject of this work, Cheng Qian focuses on applying the Python programming language to practical data science, placing the book within hands-on technical writing on programming. Books of this kind generally need to balance language fundamentals with an analysis workflow, turning ideas about data processing, analysis, and visualization into runnable code, which places certain demands on the author's programming experience and clarity of expression. Because the author's personal background, nationality, education, and professional experience are not publicly documented, no speculation is made here. On the basis of the available work, readers may regard Cheng Qian as a technical author writing around Python and practical data science, suitable for readers who wish to learn or consolidate relevant skills in a hands-on way. If more information becomes available, the author's writing trajectory and professional focus can be further described.
