Python Data Analysis in Practice

Name: Python Data Analysis in Practice
ISBN: 9787111576402

Ivan IdrisChina Machine Press2017

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Chapters: 15
Series: Data Analysis and Decision Technology Series
ISBN: 9787111576402

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Summary

This book is the Chinese translation of Python Data Analysis Cookbook by Ivan Idris, translated by Feng Bo and Yan Jiayang and published by China Machine Press in 2017 as part of the Data Analysis and Decision Technology Series. Across 12 chapters and several appendices, it uses a "problem—solution—code" cookbook format to cover practical techniques for the entire Python data analysis workflow. Chapter 1 explains how to set up a reproducible analysis environment, including Anaconda installation, virtualenv environments, Docker images, IPython Notebook, logging, and unit testing; Chapter 2 focuses on beautiful data visualization, covering Seaborn palettes, matplotlib color maps, scatterplot matrices, heatmaps, box-and-violin plot combinations, and map display; Chapter 3 discusses statistical distributions and correlations; Chapter 4 handles data anomalies and numerical computation problems; Chapter 5 covers databases, web scraping, and big data tools; Chapters 6 and 7 introduce signal processing and time series and stock financial data analysis respectively; Chapter 8 covers text mining and social network analysis; Chapters 9 and 10 explain ensemble learning, dimensionality reduction, and evaluation of classifiers, regressors, and clustering; Chapter 11 uses OpenCV for image analysis; Chapter 12 discusses parallelism and performance optimization, including caching and just-in-time compilation. Readers can read chapter by chapter or look up specific technical solutions as needed.

Editor's review

这不是一本从零讲语法的基础书，它的内容属于「配方书」类型，每个小节都给出一个真实问题、解决思路和可直接运行的代码片段。作者伊凡·伊德里斯长期从事 Python 数据技术写作，书中既有数据科学日常最常用的 pandas 配置、Seaborn 调色、热图与地图绘制，也不回避金融股票分析、文本挖掘、图像处理、并行加速等偏进阶的场景。尤其难得的是开篇就强调环境可复现：Anaconda、virtualenv、Docker、IPython 记录版本、日志与单元测试，这些是让别人的分析结果真正跑起来的关键，也是很多教程忽略的地方。如果你已经会写 pandas 和 matplotlib 的基本代码，却在真实项目中总卡在环境、异常值、性能这些琐碎环节，这本书像是一位工程师递来的工具箱。适合作为案头参考，遇到问题时按目录检索，不必从头读到尾。

Who it's for

A good fit for

已经掌握 Python 基础语法、想进入数据分析实战的开发者
会用 pandas 和 matplotlib 基本操作、需要解决实际问题的人
需要金融、文本、图像等垂直场景分析方案的从业者
希望把分析工作整理成可复现流程的数据岗位人员

Not a good fit for

完全没有 Python 编程基础的零起点读者
只想系统学习统计学理论与推导的读者
期待按章节从头到尾循序渐进讲解的教材型学习者

Reading tips

先读第1章搭好环境，再跳读与你当前项目相关的章节。
动手运行书中代码片段，按需修改参数观察结果变化。
遇到报错时对照书中版本信息排查，注意部分库 API 已随版本更新。
把可复用片段整理成自己的工具函数库，避免重复造轮子。

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.

Translator's Preface
Foreword
Chapter 1: Laying the Foundation for Reproducible Data Analysis
Chapter 2: Creating Beautiful Data Visualizations
Chapter 3: Statistical Data Analysis and Probability
Chapter 4: Handling Data and Numerical Problems
Chapter 5: Web Mining, Databases, and Big Data
Chapter 6: Signal Processing and Time Series
Chapter 7: Selecting Stocks with Financial Data Analysis
Chapter 8: Text Mining and Social Network Analysis
Chapter 9: Ensemble Learning and Dimensionality Reduction
Chapter 10: Evaluating Classifiers, Regressors, and Clustering
Chapter 11: Image Analysis
Chapter 12: Parallelism and Performance
Appendix

FAQ

这本书适合零基础学习 Python 的人吗？

不太适合。本书假设读者已能编写 Python 代码并了解 NumPy、pandas 的基础用法，重点讲如何用这些工具解决具体分析问题，缺少语法入门内容。

这本书和常见的《Python 数据分析》教材有什么区别？

本书是 cookbook 式写法，以「问题—解法—代码」组织内容，覆盖面更广，涉及 Docker、股票分析、文本挖掘、图像处理和性能优化，而非按知识点体系循序渐进讲解。

书中的代码现在还能跑吗？

核心思路仍然可用，但书中基于 2016 年前后的库版本，pandas、matplotlib、Seaborn 的接口已有变化，运行前建议查阅对应库的当前文档并适当调整。

英文原书名是什么？

原书为 Ivan Idris 的 Python Data Analysis Cookbook，中文版由冯博、严嘉阳翻译，机械工业出版社 2017 年出版。

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