Python Data Analysis and Application
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Summary
Most chapters are structured around task requirements rather than piling up knowledge points, focusing on inspiring problem-solving thinking and implementing solutions. Through experiencing this complete workflow from task requirements to implementation, readers can truly understand and internalize Python data analysis and application. All case studies are drawn from real enterprise projects and are highly practical, guiding readers to achieve mastery and providing source code and other learning resources to help them quickly acquire big data skills.
Editor's review
In terms of depth, the book covers the basic usage of mainstream libraries such as NumPy, Pandas, and Matplotlib, and provides an introductory overview of the Scikit-learn machine learning library. Although advanced topics such as deep learning and large-scale data processing are only briefly touched upon, as an introductory textbook its knowledge structure is complete and its progression reasonable. The book includes a dedicated chapter on "data ethics," reflecting the author's concern for the social responsibility of data analysis—a rarity in technical books.
Note that the book is written for Python 3.x, and the example code has good compatibility. Some case datasets are available on the publisher's website for hands-on practice. Overall, this is a solid introductory tutorial suitable for readers who wish to systematically master basic data analysis skills. Those who already have programming experience but lack data analysis experience will find it helps them quickly build a knowledge framework; practicing data analysts can keep it as a desk reference for basic operations.
Who it's for
A good fit for
- University students majoring in computer science or statistics
- Professionals hoping to transition into data analysis
- Researchers who need to use Python for basic data analysis
- Non-technical professionals who are data-sensitive and wish to improve their business analysis skills
Not a good fit for
- Readers seeking advanced machine learning or deep learning content
- Professional engineers who need to process very large datasets (e.g., terabyte-scale)
Reading tips
- Read the chapters in order and focus on practicing the code examples in each chapter
- Expand practice with public datasets such as Kaggle beginner competition data
- When unsure about library function usage, consult the official documentation first as a supplement
About the publisher
Posts & Telecom Press is known for its strengths in science and technology, computing, and information and communications titles, and has extended its publishing into photography, art, software learning, and inspirational growth, giving it a fairly diverse range. Its catalog includes computing and communications works such as Refactoring and Patterns (Revised Edition) and Huawei Switch Learning Guide (Second Edition), alongside bilingual collector's editions such as The Little Prince (Chinese-English-French illustrated collector's edition), The Great Gatsby, and Three Days to See. Beijing Ten Years: 1980-1990 and Ghost Blade: WLOP Personal Illustration Collection reflect its presence in photography and art. Its authors include Antoine de Saint-Exupéry, Helen Keller, Joshua Kerievsky, Wang Da, Zhang Zhaozeng, and WLOP.
Contents
- 版权信息
- 内容提要
- 大数据专业系列图书编写委员会
- 序
- 前言
- 第1章 Python数据分析概述
- 任务1.1 认识数据分析
- 任务1.2 熟悉Python数据分析的工具
- 任务1.3 安装Python的Anaconda发行版
- 任务1.4 掌握Jupyter Notebook常用功能
- 小结
- 课后习题
- 第2章 NumPy数值计算基础
- 任务2.1 掌握NumPy数组对象ndarray
- 任务2.2 掌握NumPy矩阵与通用函数
- 任务2.3 利用NumPy进行统计分析
- 小结
- 实训
- 课后习题
- 第3章 Matplotlib数据可视化基础
- 任务3.1 掌握绘图基础语法与常用参数
- 任务3.2 分析特征间的关系
- 任务3.3 分析特征内部数据分布与分散状况
- 小结
- 实训
- 课后习题
- 第4章 pandas统计分析基础
- 任务4.1 读/写不同数据源的数据
- 任务4.2 掌握DataFrame的常用操作
- 任务4.3 转换与处理时间序列数据
- 任务4.4 使用分组聚合进行组内计算
- 任务4.5 创建透视表与交叉表
- 小结
- 实训
- 课后习题
- 第5章 使用pandas进行数据预处理
- 任务5.1 合并数据
- 任务5.2 清洗数据
- 任务5.3 标准化数据
- 任务5.4 转换数据
- 小结
- 实训
- 课后习题
- 第6章 使用scikit-learn构建模型
- 任务6.1 使用sklearn转换器处理数据
- 任务6.2 构建并评价聚类模型
- 任务6.3 构建并评价分类模型
- 任务6.4 构建并评价回归模型
- 小结
- 实训
- 课后习题
- 第7章 航空公司客户价值分析
- 任务7.1 了解航空公司现状与客户价值分析
- 任务7.2 预处理航空客户数据
- 任务7.3 使用K-Means算法进行客户分群
- 小结
- 实训
- 课后习题
- 第8章 财政收入预测分析
- 任务8.1 了解财政收入预测的背景与方法
- 任务8.2 分析财政收入数据特征的相关性
- 任务8.3 使用Lasso回归选取财政收入预测的关键特征
- 任务8.4 使用灰色预测和SVR构建财政收入预测模型
- 小结
- 实训
- 课后习题
- 第9章 家用热水器用户行为分析与事件识别
- 任务9.1 了解家用热水器用户行为分析的背景与步骤
- 任务9.2 预处理热水器用户用水数据
- 任务9.3 构建用水行为特征并筛选用水事件
- 任务9.4 构建行为事件分析的BP神经网络模型
- 小结
- 实训
- 课后习题
- 附录A
- 附录B
- 参考文献
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About the author
Huang Hongmei is the author of Python Data Analysis and Application. Publicly available information about her is limited: beyond this single work, little can be confirmed about her background, education, professional position, or other publications. The book is classified under general computing, and its title suggests a practical focus on using Python for data analysis, likely covering common steps such as data reading, cleaning, processing, and visualization for readers who want to handle analytical tasks through programming. Given the available clues, her writing appears oriented toward hands-on technical practice and tool application, emphasizing the combination of a programming language with a data analysis workflow. Books of this kind usually rely on examples and step-by-step operations, making them suitable for beginners or practitioners seeking a quick start. Because only one title by her is included here, it is difficult to assess her overall style or academic influence.
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.
