Quick Start to Intelligent Data Analysis with Python 3

Name: Quick Start to Intelligent Data Analysis with Python 3
ISBN: 9787111628057

Li Mingjiang / 张良均 / 周东平 / Zhang ShangjiaChina Machine Press2019

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Word count: 305K words
Chapters: 10
Series: Intelligent Systems and Technologies Series
ISBN: 9787111628057

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Summary

Published by China Machine Press in 2019 as part of the Intelligent Systems and Technologies series, this book is aimed at readers with no Python programming background and no foundation in artificial intelligence, introducing the techniques and methods needed for intelligent data analysis with Python 3 in a progressive, step-by-step manner. The book has nine chapters, logically divided into two parts. Part One (Chapters 1–4) covers Python programming fundamentals: Python's language features and application areas; setting up and using Anaconda, PyCharm, and Jupyter Notebook; fixed syntax such as encoding declarations, comments, and indentation; operators and data types; conditional statements, loops, and exception handling; built-in functions, user-defined functions, anonymous functions, object-oriented programming, and installing and calling third-party libraries. Part Two (Chapters 5–9) covers data analysis programming: the ndarray and ufunc of the numerical computing library NumPy; data preparation with the data-processing library pandas—Series, DataFrame, reading and writing data, group operations, pivot tables, and missing values; common charts and interactive visualization with Matplotlib, Seaborn, and Bokeh; and data preprocessing, dimensionality reduction, clustering, classification, regression, and model selection in scikit-learn, basically covering the full workflow from data reading and preprocessing to modeling, evaluation, and visualization of results. Each chapter ends with a summary and exercises, with accompanying sample code and data for learning by doing.

Editor's review

这本书的定位很清楚：它不是一本泛泛的 Python 语法书，也不是一本只讲算法公式的机器学习教材，而是一条从“会写 Python”到“能做数据分析与建模”的完整通路。前四章把环境搭建、语法、函数与面向对象、第三方库安装这些最容易卡住新手的基础一次性讲透，Anaconda、PyCharm、Jupyter Notebook 都有单独篇幅，读者照做即可跑通。后五章则按数据分析的真实流程展开：先用 NumPy 打数值计算的地基，再用 pandas 章节完成数据读取、清洗与分组统计，接着用 Matplotlib、Seaborn、Bokeh 把结果画出来，最后用 scikit-learn 做降维、聚类、分类、回归与模型评估。作者团队长期从事数据挖掘教学与项目实践，写法偏重“函数怎么用、结果怎么解释”，示例代码密度高，每章后还配课后习题，很适合作为课程教材或自学路线图。需要提醒的是，书中示例基于 2019 年前后的库版本，动手时建议对照当前版本的 NumPy、pandas 与 scikit-learn 文档做少量适配。

Who it's for

A good fit for

零编程基础但想转入数据分析岗位的转行者
开设有数据分析、机器学习课程的高校师生
会一点 Python 语法、需要系统补齐 pandas 与建模能力的自学者
从事统计报表、业务分析，希望把工作流程迁移到 Python 的职场人

Not a good fit for

已熟练使用 pandas 与 scikit-learn、想深入原理的中高级数据从业者
只想要直接上手大模型、深度学习框架的读者
期待完整行业项目案例与业务建模全流程的读者

Reading tips

前四章可按需快读：有编程基础者可跳到第2.3节数据类型与第4章函数部分查漏补缺。
第5—9章务必边读边敲代码，先跑通书中示例，再换成自己的 CSV 数据重做一遍。
把 pandas 三章（第6、7章）当作手册反复查阅，数据清洗与分组统计是日常用得最多的部分。
读到第9章时，可挑一个熟悉的小数据集完整走一遍标准化、建模、交叉验证与评估的流程。

About the publisher

China Machine Press China · Founded in 1950

In addition to science and technology and professional publishing, the books in our catalog from China Machine Press also reflect a sustained focus on psychology, computing, management, finance and economics, and social sciences. In psychology, the catalog includes works by C. G. Jung and introductions to analytical psychology, as well as applied topics such as stress and health, and addiction and healing. In computing, it features Structure and Interpretation of Computer Programs (JavaScript Edition). Management and finance titles include Administrative Behavior (4th Edition), The Price of Inequality (Collector's Edition), and This Time Is Different: Eight Centuries of Financial Folly (Commemorative Edition). In the philosophy and history of science, there is The Worldview: The Third Edition. Relevant authors include C. G. Jung, William McGuire, Sonu Shamdasani, and Gabor Maté. Public information is limited; the above summarizes the publisher's profile based solely on the books in our catalog.

Preface
Chapter 1: Overview of Python
Chapter 2: Python Basics
Chapter 3: Control Statements
Chapter 4: Functions and Objects
Chapter 5: Numerical Computing with NumPy
Chapter 6: pandas Basics
Chapter 7: Advanced pandas
Chapter 8: Plotting
Chapter 9: scikit-learn

FAQ

这本书需要先学 Python 才能读吗？

不需要。作者明确面向零 Python 基础读者，第1—4章从环境搭建、语法、控制语句讲到函数与对象，按顺序读即可；有基础的可直接跳到第5章 NumPy。

书里的代码和现在最新的库版本兼容吗？

本书 2019 年出版，示例基于当时的 Python 3 与 NumPy、pandas、scikit-learn 版本。部分接口在新版中已调整，运行报错时建议对照官方文档修改参数名。

这本书偏理论还是偏实操？

偏实操。第二部分重点讲各库常用函数的用法与结果解释，配有示例代码与数据，每章后附课后习题，理论推导较少。

读完能直接做机器学习项目吗？

能掌握数据读取、预处理、特征处理、常用模型训练与评估的基本操作；但书中案例规模较小，复杂业务项目还需补充特征工程与调参经验。

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About the author

张良均 Author

Zhang Liangjun is a Chinese author of books on computing and data analysis. Public information about him is limited, and his biography, nationality, and educational background remain unclear. Judging from the seven titles held in the collection, his writing centers on two areas: programming design and data processing. His works range from introductory programming topics such as Python and R to practical content on Excel data acquisition, processing, and analysis, as well as Power BI data analysis and visualization. Aimed at beginners and everyday office application scenarios, the books combine tool operations with examples and emphasize a gradual path from basic syntax to practical analysis, while also serving exam and teaching needs. These titles fall under categories including programming design, general computing, and examinations, and are suitable for Chinese readers who wish to systematically learn data analysis tools and programming fundamentals.

周东平 Author

Zhou Dongping: public information about this author is limited. At present, only one work is known, A Study of the Legal History of the Han and Tang Dynasties, which is classified as a history title. Judging from the title, the writing focuses on the legal systems from the Han through the Tang dynasty and falls within the field of Chinese legal history, touching on statutes and ordinances, judicial practice, and institutional change. Work of this kind usually requires the author to cross-check transmitted texts against excavated materials in order to present the legal order of the early imperial era as a whole. On the available evidence, Zhou Dongping appears to treat this period through focused topical research, with an emphasis on factual reconstruction and institutional analysis rather than popular narrative. Because no reliable record of his life, education, or positions is available, this note offers no speculation and merely outlines, from the library's holdings, the direction of his writing for readers' reference.