Learn Python with Lao Qi: Data Analysis

Name: Learn Python with Lao Qi: Data Analysis
ISBN: 9787121340031

齐伟Publishing House of Electronics Industry2018

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Word count: 541 words
Chapters: 40
Series: Learn Python with Lao Qi
ISBN: 9787121340031

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Summary

This book is a follow-up in the "Learn Python with Lao Qi" series, aimed at readers who already have basic Python syntax and want to extend their language skills into data analysis. The book begins with data-related concepts and the development of data analysis technology, first guiding readers through development environment setup, then unfolds in three parts: first, NumPy, covering array objects, indexing and slicing, array operations, computations and universal functions, simple statistics, matrices, and vector operations; second, Pandas, covering common data objects such as Series and DataFrame, index objects, data indexing and slicing, file reading and writing, missing data handling, data wrangling, group operations, vectorized string processing, and time-related operations; third, data visualization, centered on Matplotlib and covering coordinate system settings, plotting methods, and common statistical charts, with brief mention of 3D graphics and Seaborn. Chapter 4 ties the preceding knowledge together with several comprehensive cases, including analysis and visualization of stock data, review data, movie box office data, urban population data, and wine data, as well as applications of Benford's law and word cloud creation. Chapter 5 further introduces introductory machine learning content such as linear regression, logistic regression, and Bayesian methods, providing a transition from data analysis to modeling. The book continues the author's accessible, learn-by-doing style, emphasizing mastery of library object attributes and methods and comprehensive application through examples.

Editor's review

很多数据分析入门书一上手就抛公式和术语，读起来像在啃说明书。这本书走的是另一条路：它假定你已经会写Python，会循环、会函数、会处理列表字典，然后把注意力集中在真正要用的几件工具上，NumPy、Pandas、Matplotlib，一个不落地把它们的对象、属性和方法讲清楚。作者的写作习惯是边讲边给代码、边给结果，读者可以照着敲一遍，很快就看到一个能跑起来的东西。结构也很清楚：前三章打地基，第4章用七个真实感较强的案例把知识缝合起来，第5章顺势摸到线性回归、Logistic回归和贝叶斯方法的门槛。对于不想被数学推导拦住、更想先把工具用起来的读者，这种“可用优先”的取舍恰到好处。需要提醒的是，本书出版于2018年，示例基于当时的库版本，照抄代码时可能遇到版本差异，动手时需要自己查文档、自己调试。这也正是练习的一部分。

Who it's for

A good fit for

已掌握Python基本语法、想转向数据分析的读者
需要系统梳理NumPy与Pandas用法的初级数据从业者
想用Python完成数据清洗、统计与图表的在校学生
希望以案例方式入门机器学习基础方法的自学者

Not a good fit for

完全没有Python基础、连循环和函数都不熟悉的纯新手
期待深入学习统计理论、推导算法数学原理的读者
需要深度学习、大数据平台等前沿工程内容的从业者

Reading tips

先读第0章配置好环境，再按NumPy、Pandas、可视化的顺序推进，不要跳读。
每章的示例代码务必亲手敲一遍，改参数、换数据，观察结果变化。
第4章案例当作练习，先合上书自己写，再对照书中做法查漏补缺。
注意库版本差异，遇到报错及时查阅当前官方文档，养成查文档的习惯。

About the publisher

Publishing House of Electronics Industry China · Founded in 1982

The Publishing House of Electronics Industry is a comprehensive publisher distinguished by its focus on electronic information and related fields. Judging from the more than 3,000 titles in its catalogue, its publishing covers programming and design, artificial intelligence, computer science, finance and economics, photography, and social sciences, attending both to cutting-edge technology and to general readers. Representative titles include Factor Investing: Methods and Practice, x86 Assembly Language: From Real Mode to Protected Mode, The RISC-V Reader, Artificial Intelligence: Speech Recognition, Understanding and Practice, Phased Array Radar Technology, Radar Target Characteristics, and 100 Go Mistakes and How to Avoid Them, most of which centre on technical methods and engineering practice. Its authors include Shi Chuan, Li Zhong, Wang Xiaobo, David Patterson, Andrew Waterman, and Yu Zihao, among others from China and abroad. Owing to limited public information, no further publishing features are elaborated here.

Chapter 0 Overview of Data Analysis
0.1 Data-Related Concepts
0.2 Development of Data Analysis Technology
0.3 Development Environment Setup
Chapter 1 NumPy Basics and Applications
1.1 Basics of Array Objects
1.2 Array Indexing and Slicing
1.3 Operations on Arrays
1.4 Computations and Universal Functions
1.5 Simple Statistical Applications
1.6 Matrices
1.7 Vector Operations
1.8 Comprehensive Application Examples
Chapter 2 Pandas Basics and Applications
2.1 Common Data Objects
2.2 Index Objects
2.3 Data Indexing and Slicing
2.4 File Reading and Writing Operations
2.5 Handling Missing Data
2.6 Data Wrangling
2.7 Group Operations
2.8 Vectorized Strings
2.9 Time-Related Operations
2.10 Simple Application Examples
Chapter 3 Data Visualization
3.1 Matplotlib Overview
3.2 Setting the Coordinate System
3.3 Plotting Images
3.4 Common Statistical Charts
3.5 Plotting 3D Images
3.6 A Glimpse of Seaborn
Chapter 4 Comprehensive Applications
4.1 Analyzing Stock Data
4.2 Analyzing Bra Review Data
4.3 Analyzing Movie Box Office Data
4.4 Visualizing Urban Population Data
4.5 Analyzing Greek Wine Data
4.6 Applying Benford's Law
4.7 Creating Word Clouds
Chapter 5 Machine Learning

FAQ

这本书适合零基础学Python的人吗？

不太适合。书中默认读者已掌握Python基本语法，包括列表、字典、函数、循环等，直接进入NumPy和Pandas，没有语言入门章节。建议先读完《跟老齐学Python：从入门到实践》一类基础书，再来读这本。

书里主要讲了哪些库？

按官方内容介绍，主要涉及NumPy、Pandas、matplotlib、SciPy、SymPy等与数据分析相关的库，目录中重点展开的是NumPy、Pandas和Matplotlib，并简要介绍Seaborn。

这本书有配套代码或习题吗？

书中各节配有示例代码和综合应用案例，可以照着复现；但据现有公开资料，未明确说明有单独出版的习题册。作者在GitHub上以qiwsir为名发布过Python学习相关资料，读者可以去其主页查找补充材料。

2018年出版的书，现在学还有用吗？

核心概念和库的基本用法依然适用，但Pandas、Matplotlib等库版本迭代较快，书中部分写法在新版本中可能已弃用。建议以书中思路为主，具体函数调用以当前官方文档为准。

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About the author

齐伟 Author

Qi Wei is an author about whom little public information is available; no systematic account of his identity or career has yet appeared. His two works in the library nonetheless indicate his main writing interests. He focuses on artificial intelligence and programming design, and is the author of Data Preparation and Feature Engineering: Essential Skills for Data Engineers and Mathematical Foundations of Machine Learning. The first addresses practical aspects of data engineering, including data cleaning, organization, and feature construction; the second returns to the mathematical principles behind machine learning, helping readers build a theoretical foundation. Together, the two books show a style that values both fundamentals and hands-on practice, with clear and accessible language suitable for beginners and advanced readers alike.