Python Fundamentals and Big Data Applications

Name: Python Fundamentals and Big Data Applications
ISBN: 9787115517388

丁辉 / 商俊燕 / 伍转华 / 林雪纲Posts & Telecom Press · ChinaJul 2021

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Word count: 104K words
Chapters: 247
ISBN: 9787115517388

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Summary

The content is organized to follow students' cognitive patterns and, in light of Python's characteristics, divides the material into two main parts: Python fundamentals and data analysis. The book has 11 chapters. The first six cover basic Python syntax and applications; the last five systematically explain how to crawl, process, and analyze data with Python. In addition to abundant examples, each chapter includes practice-oriented project exercises and extension projects to help readers apply what they learn. This book can serve as a textbook for computer science and related majors in higher education, as well as a self-study guide for Python enthusiasts.

Editor's review

Python Basics and Big Data Applications is a practical technical book for beginners published by Posts & Telecom Press in 2019. Author Ding Hui brings extensive teaching and hands-on experience, which helps the book strike a proper balance between depth and breadth. In terms of structure, it is clearly divided into two modules: Python language fundamentals and big data application practice. This progressive arrangement, from basic syntax to typical application scenarios, closely follows how learners actually absorb new material.

The book's core value lies in its "application-oriented" approach. Rather than simply listing grammar points, it treats Python as a tool and builds tightly around the real needs of data processing, analysis, and visualization. Its coverage of core data science libraries such as NumPy, Pandas, and Matplotlib is especially solid, and the case studies stay close to real business scenarios—data cleaning, statistical analysis, charting, and so on—which makes the book highly instructive for readers who want to quickly start solving practical problems.

That said, it should be noted objectively that the book's treatment of "big data" stays largely at the level of single-machine tools, with only limited coverage of distributed computing frameworks such as Hadoop and Spark. It may therefore be more accurately described as a "beginner-to-intermediate guide to Python data science applications." The language is plain, the steps are detailed, and the figures are clear, lowering the barrier to learning. Overall, this is a high-quality introductory and intermediate practical guide, especially suited to professionals and students who need to apply Python quickly to data analysis work.

Who it's for

A good fit for

Beginners who want to learn Python systematically and apply it to data analysis and processing
Professionals with some programming background who want to move into data science or data analysis
Readers who need a reference book combining basic syntax with hands-on use of data science libraries such as Pandas and NumPy
University students in computer science, statistics, economics and management, and related majors who are taking courses in Python and data analysis

Not a good fit for

Readers seeking in-depth explanation of machine learning and deep learning algorithm principles and advanced implementations
Readers who want to systematically learn the core technologies of distributed big data frameworks such as Hadoop and Spark

Reading tips

Reading in chapter order is recommended: build a solid foundation in Python syntax first, then move on to the data analysis libraries
Be sure to run the code and case studies in the practice sections yourself—this is key to mastering the book's content
The book can serve as a core textbook, supplemented by official documentation and online communities to resolve deeper questions encountered while learning

About the publisher

Posts & Telecom Press China · Founded in 1953

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.

版权信息
内容提要
前言
第1章 Python环境搭建
1.1 Python版本概述及下载Python安装文件
1.1.1 Python版本概述
1.1.2 下载Python安装文件
1.2 安装Python
1.2.1 解压下载的文件
1.2.2 运行Python安装文件
1.3 Python交互模式
1.4 iPython 3 和PyCharm概述
1.4.1 iPython 3 概述
1.4.2 PyCharm概述
1.5 项目训练：Python的安装与使用
1.6 本章小结
1.7 练习
1.8 拓展训练项目
第2章 Python编程基础
2.1 变量
2.2 数值
2.2.1 整型
2.2.2 浮点型
2.2.3 复数型
2.2.4 布尔型
2.3 字符串
2.3.1 转义字符
2.3.2 字符串运算
2.3.3 字符串操作方法
2.4 列表
2.4.1 列表的创建与删除
2.4.2 列表操作方法
2.4.3 列表切片操作
2.5 元组
2.6 字典
2.6.1 字典的创建与访问
2.6.2 字典元素的修改
2.6.3 字典操作方法
2.7 运算符
2.7.1 算术运算符
2.7.2 位运算符
2.7.3 逻辑运算符
2.7.4 比较运算符
2.7.5 赋值运算符
2.7.6 其他运算符
2.8 Python代码编写规范
2.9 控制流
2.9.1 顺序结构程序
2.9.2 分支结构程序
2.9.3 循环结构程序
2.10 项目训练：个人所得税计算
2.11 本章小结
2.12 练习
2.13 拓展训练项目
2.13.1 Python数值型变量的定义与赋值
2.13.2 Python控制流和运算符
2.13.3 列表的基本操作
2.13.4 元组的基本操作
2.13.5 字典的基本操作
2.13.6 字符串的基本操作
第3章函数
3.1 自定义函数
3.1.1 函数定义格式
3.1.2 函数的设计
3.1.3 lambda表达式
3.2 函数调用
3.3 函数参数
3.3.1 位置参数
3.3.2 默认参数
3.3.3 关键参数
3.3.4 可变长度参数
3.4 变量作用域
3.4.1 局部变量
3.4.2 全局变量
3.5 异常
3.5.1 Python标准异常类
3.5.2 异常处理
3.6 项目训练：哥德巴赫狂想 ——任何大于 2 的偶数总可以分解成两个素数的和
3.7 本章小结
3.8 练习
3.9 拓展训练项目
3.9.1 用函数实现乘法口诀
3.9.2 Python函数参数
3.9.3 Python局部变量和全局变量
3.9.4 Python异常捕获与处理
第4章面向对象编程基础
4.1 类和对象
4.1.1 类
4.1.2 对象
4.2 属性与方法
4.2.1 属性
4.2.2 方法
4.3 继承
4.4 多态
4.5 项目训练：简单学生成绩管理系统
4.6 本章小结
4.7 练习
4.8 拓展训练项目
4.8.1 Python类与对象
4.8.2 类方法、实例方法和静态方法
4.8.3 类继承、组合
4.8.4 类的多重继承
第5章模块
5.1 模块的创建和命名空间
5.1.1 模块的创建
5.1.2 命名空间
5.2 模块的导入和路径
5.2.1 模块的导入
5.2.2 模块的路径
5.3 包
5.4 Python内置模块
5.4.1 math模块
5.4.2 random模块
5.4.3 time模块
5.4.4 datetime模块
5.4.5 calendar模块
5.4.6 sys模块
5.4.7 zipfile模块
5.5 项目训练：日历
5.6 本章小结
5.7 练习
5.8 拓展训练项目
5.8.1 Python模块导入
5.8.2 zipfile模块的使用
5.8.3 Python模块的属性
5.8.4 Python模块内置函数
第6章 Python文件和数据库
6.1 文件的基本操作
6.1.1 内置函数open()
6.1.2 文件对象常用的属性和方法
6.1.3 文件操作案例
6.2 文件系统的基本操作
6.3 MySQL数据库
6.3.1 MySQL简介
6.3.2 安装MySQL
6.3.3 使用Python连接MySQL数据库
6.3.4 MySQL的基本操作
6.4 项目训练：使用Python完成课程表和学生信息表的创建
6.5 本章小结
6.6 练习
6.7 拓展训练项目
6.7.1 安装MySQL数据库和Python连接数据库
6.7.2 使用Python实现MySQL增查改删
6.7.3 Python文件的基本操作
6.7.4 Python文件目录的基本操作
第7章 Python爬虫基础
7.1 网络爬虫概述及其结构
7.1.1 网络爬虫概述
7.1.2 网络爬虫结构
7.2 urllib库
7.2.1 urllib.request模块
7.2.2 urllib.parse模块
7.2.3 urllib.error模块
7.3 使用urllib爬取网页
7.4 浏览器的模拟与实战
7.5 正则表达式
7.6 图片爬虫实战
7.7 项目训练：用urllib库爬取百度贴吧
7.8 本章小结
7.9 练习
7.10 拓展训练项目
7.10.1 urllib库的使用
7.10.2 百度贴吧网页爬虫
7.10.3 淘宝网站图片爬虫
第8章 Python爬虫框架
8.1 常见爬虫框架
8.2 Scrapy爬虫框架的安装
8.3 Scrapy爬虫框架简介
8.4 Scrapy常用工具命令
8.4.1 创建一个Scrapy项目
8.4.2 Scrapy全局命令
8.4.3 Scrapy项目命令
8.5 Scrapy爬虫实战
8.6 项目训练：用Scrapy爬取豆瓣图书
8.7 本章小结
8.8 练习
8.9 拓展训练项目
8.9.1 Scrapy框架的安装及使用
8.9.2 Scrapy命令行工具
第9章数据分析基础
9.1 numpy模块
9.1.1 ndarray类型数组
9.1.2 matrix类型矩阵
9.1.3 matrix类型和array类型的区别
9.2 pandas模块
9.2.1 pandas模块基础
9.2.2 pandas模块数据清洗
9.2.3 pandas模块数据预处理
9.2.4 pandas模块数据提取
9.2.5 pandas模块数据筛选
9.2.6 pandas模块数据汇总
9.2.7 pandas模块数据统计
9.2.8 pandas模块综合应用示例
9.3 项目训练：清洗和预处理 8.6 节中爬取的doubanread.csv文件
9.4 本章小结
9.5 练习
9.6 拓展训练项目
9.6.1 pandas 基本功能实验
9.6.2 pandas 汇总和计算实验
9.6.3 pandas 缺失数据处理
9.6.4 pandas 构建层次化索引
第10章 pandas数据分析
10.1 pandas文件读写基础
10.1.1 CSV文件的读写
10.1.2 Excel文件的读写
10.2 pandas与MySQL数据库的交互
10.2.1 pandas与MySQL连接的步骤
10.2.2 pandas与MySQL交互
10.3 pandas字符串处理
10.4 pandas数据分组与聚合
10.4.1 使用内置的聚合函数进行聚合运算
10.4.2 分组与聚合过程
10.4.3 agg()和apply()聚合函数
10.5 项目训练：电影数据统计
10.6 本章小结
10.7 练习
10.8 拓展训练项目
10.8.1 pandas文件读写
10.8.2 pandas数据库读写
10.8.3 pandas数据处理
10.8.4 pandas数据聚合和组迭代
第11章 Python可视化与可视化工具
11.1 Python可视化与可视化工具介绍
11.2 pandas基本图形绘制
11.2.1 折线图
11.2.2 柱状图
11.2.3 直方图
11.2.4 散点图
11.2.5 面积图
11.2.6 饼图
11.2.7 密度图
11.3 matplotlib绘图
11.3.1 matplotlib绘图基础
11.3.2 matplotlib交互绘图
11.4 matplotlib.pyplot的使用
11.4.1 pyplot绘图基础
11.4.2 多种类型图的绘制
11.5 项目训练：电影数据信息分析
11.6 本章小结
11.7 练习
11.8 拓展训练项目
11.8.1 pandas绘图
11.8.2 matplotlib交互式绘图实践
11.8.3 pyplot绘图元素的设置
11.8.4 子图的绘制
附录
参考文献

FAQ

Is this book suitable for complete programming beginners?

Very suitable. The book starts with setting up the Python environment and basic syntax, proceeds step by step, and includes detailed case studies—very friendly to beginners.

What level can I reach after finishing this book?

You will be proficient in core Python syntax and able to use libraries such as Pandas and NumPy to independently complete general data cleaning, analysis, and visualization tasks, giving you the foundational practical skills to enter the field of data science.

What makes this book distinctive compared with similar titles?

Its distinction lies in seamlessly connecting Python fundamentals with data science applications, with a clear goal (serving data analysis) and strong practicality. It avoids the common problems of learning syntax in isolation or jumping into applications without a foundation.

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About the author

丁辉 Author

Ding Hui (public information on the author's English name, nationality, dates of birth and death, and similar details is limited and cannot be further confirmed) is an author whose works span several fields. Three titles are included in the collection, covering cross-border e-commerce, programming and big data applications, and inspirational personal growth. Among them, Cross-Border E-Commerce Multi-Platform Operations: Practical Basics (4th Edition) focuses on the practical fundamentals of multi-platform operations; Python Basics and Big Data Applications is aimed at programming beginners and data applications; and Reversing the Opening is oriented toward personal growth and inspiration. Ding Hui's writing thus combines practicality with readability, addressing management and operations as well as information technology, while also touching on growth topics for general readers. The works commonly appear in categories such as management, examinations, and inspirational growth, and are suited to readers seeking practical methods or self-improvement. Because public information is limited, further details about the author's personal background cannot be provided.

商俊燕 Author

Shang Junyan is an author about whom limited public information is available. Her one confirmed work is Python Basics and Big Data Applications, which is catalogued under exam preparation, suggesting that its content is aimed at test preparation, course study, or related skills assessment. Judging from the title, the book covers both the fundamentals of Python programming and big data applications, and its writing appears to focus on systematically organizing key knowledge points and providing introductory training in practical skills, so that readers can progress step by step toward their study or examination goals. Since only this one work is included and details such as nationality, dates of birth and death, and English name cannot be verified, no further information about the author's educational background, professional identity, or writing career can be provided. Overall, Shang Junyan's writing centers on textbook and exam-preparation materials, suitable for readers with clear learning or test-preparation needs.

伍转华 Author

Wu Zhuanhua is an author about whom limited public information is available; the library currently holds only one work by this author, Python Basics and Big Data Applications. Judging from the title and the common category of examinations, the book appears to address the needs of beginners learning programming and the fundamentals of big data, combining basic language instruction with guidance on practical application. As no information on birth and death dates, nationality, education, or professional experience can be confirmed, this profile makes no speculation and invents no details about mentorship or career background. Based on the available clues, Wu Zhuanhua's writing follows a practical, textbook-style approach, linking Python syntax with basic methods of big data processing for course study or examination preparation. Readers may regard the work as an introductory reference, while a fuller account of the author's life and writing remains to be supplied by public sources.

林雪纲 Author

Lin Xuegang is an author active in writing books on computing and data analysis. Public information about him is limited: his nationality, dates of birth and death, education, and professional positions are not clearly recorded, and only four works are known under his name. These works cover mobile development, programming languages, data processing, and big data technologies, including Android and Hadoop development tutorials that combine case studies with online experiments, an introduction to Python fundamentals and big data applications, and a practical guide to data analysis based on Excel. His writing clearly emphasizes hands-on practice and case-based exercises, often paired with online experiments and self-tests so that readers can learn by doing. In book classifications, his works are usually placed under general computing and examination categories, making them suitable as course textbooks or self-study references.