Python Programming Fundamentals and Data Analysis

Name: Python Programming Fundamentals and Data Analysis
ISBN: 9787568937344

赵胜利 / 苏理云 / 邱世芳Chongqing University Press · ChinaMar 2024

3.4

Word count: 117K words
Chapters: 109
ISBN: 9787568937344

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Summary

This book provides a detailed introduction to the fundamentals of Python programming, the basic tools for data processing and analysis in Python, the common methods and principles of statistical modeling, and their implementation in Python. The final two chapters present two practical modeling case studies, offering a detailed walkthrough of the entire statistical modeling process: data preprocessing, feature engineering, statistical modeling, and result analysis and visualization.

This book is suitable for undergraduate and graduate students and instructors in statistics, applied mathematics, economics and management, and computer science, as well as for professionals working in these fields.

Editor's review

Python Programming Fundamentals and Data Analysis is a clearly positioned, introductory practical guide. According to online sources, the book is published by Chongqing University Press, and the author, Zhao Shengli, has front-line teaching and project experience, which gives the book a clear pedagogical logic and a practice-oriented character in its organization of knowledge. Unlike many books on the market that merely list syntax or show off data analysis cases, this book starts with Python environment setup and basic syntax, progresses gradually to core data analysis libraries such as NumPy, Pandas, and Matplotlib, and ultimately lands on practical data processing and visualization cases. This step-by-step path, which emphasizes solid foundations, is particularly helpful for building a systematic knowledge framework.

The core value of this book lies in its balance: it ensures solid training in programming syntax fundamentals while staying closely tied to the application goal of data analysis, avoiding any disconnect between learning and application. Most examples use data scenarios drawn from everyday life, lowering the barrier to understanding and making learning more relatable. For self-learners, the clear step-by-step explanations and relatively complete project practice guidance can effectively reduce early confusion. However, it is not a book that pursues depth in cutting-edge technologies; its content focuses more on building a solid, reliable foundation and guiding readers into the field. Overall, this is a recommended, steady introductory book on Python data analysis, especially suitable for readers who want to learn systematically and quickly apply their knowledge to practical data processing tasks.

Who it's for

A good fit for

Beginners with no programming background who want to systematically learn Python and perform data analysis
Students and researchers in economics, management, statistics, and other non-computer-science fields who need to master data analysis tools
Professionals who need to process data and create reports at work and want to improve their automation capabilities
Self-learners looking for an introductory textbook with a clear structure, ample exercises, and hands-on practice

Not a good fit for

Readers who have already mastered basic Python syntax and are looking for in-depth coverage of advanced data analysis models (such as machine learning)
Experienced developers who want a quick API reference or a large number of complex real-world project cases

Reading tips

It is recommended to study strictly in chapter order and to complete the code examples and exercises in each chapter by hand to consolidate basic concepts.
When learning data analysis libraries (such as Pandas), find small datasets related to your own work or interests for repeated practice to deepen understanding.
When reading the comprehensive cases in the latter part of the book, do not be satisfied with merely reproducing the results; try adjusting parameters, changing methods, and considering the pros and cons of different solutions.

About the publisher

Chongqing University Press China · Founded in 1985

Chongqing University Press is a comprehensive university press whose publishing areas span social and cultural studies, industrial technology, finance and economics, medicine, social psychology, and theory. Judging from its catalog, the press publishes both academic works and popular science titles. Representative books include E. H. Gombrich's Shadows: The Depiction of Cast Shadows in Western Art, Magnesium-Based Hydrogen Storage Materials by Pan Fusheng et al., Research on China's Regional Spatial Development Models by Sun Jiuwen, and titles from the Gynecologic Oncology Committee of the Chinese Anti-Cancer Association, such as the Guidelines for Diagnosis and Treatment of Common Gynecologic Malignancies in China (2019) and Bipolar Disorder: What You and Your Family Need to Know. Its authors work in art history, materials science, regional economics, clinical medicine, and social research methods. Public information is limited, but its publishing profile combines academic translation, industrial technology, and practical medical guides.

版权信息
内容提要
前言
1 Python数据分析概述和Anaconda
1.1 数据分析概述
1.2 Python数据分析工具
1.3 Python的Ananconda发行版
1.4 本书的内容安排
2 Python中的变量和数据类型
2.1 print函数
2.2 变量
2.3 变量命名规则
2.4 简单数据类型
2.5 字符串
2.6 数据类型转换
习题
3 基本语法
3.1 缩进
3.2 操作符与运算符
3.3 数值运算及其优先级
3.4 函数
3.5 模块
3.6 获取输入内容
3.7占位符
3.8 进制以及之间的转换
3.9 注释
习题
4 列表
4.1自由的列表
4.2 元素访问和修改
4.3 列表元素的增加
4.4 列表元素的删除
4.5 列表元素的嵌套
4.6 名字绑定规则
4.7 元素排序
4.8 数值列表
4.9 列表统计
4.10 快速创建列表
4.11 列表运算
4.12 成员函数
4.13 列表的复制
4.14 列表的切片
习题
5 数据类型和标签式存储
5.1 元组的定义
5.2 bytes
5.3 序列和名字绑定
习题
6 条件语句和循环语句
6.1 条件语句
6.2 断言和Python之禅
6.3 循环语句
6.4 几个特殊函数
习题
7 字典
7.1 字典的创建
7.2 dict函数
7.3 字典的基本操作
7.4 字典的嵌套
7.5 字符串映射替换
7.6 成员函数
7.7 字典的遍历
习题
8 函数
8.1 自定义函数
8.2 函数参数
8.3 作用域
8.4 递归
8.5 存函数于模块
习题
9 数值计算基础——NumPy
9.1 NumPy中的数组对象ndarray
9.2 NumPy矩阵与通用函数
9.3 利用NumPy进行统计分析
习题
10 Matplotlib数据可视化
10.1 pyplot绘图基本语法与常用函数
10.2 用图形展示特征之间的关系
10.3 用图形展示数据分布与分散情况
习题
11 统计分析基础工具——pandas
11.1 用pandas读写不同源的数据
11.2 DataFrame的常用操作
11.3 时间序列数据
11.4 分组聚合进行组内计算
11.5 透视表与交叉表的创建
习题
12 使用pandas进行数据预处理
12.1 数据合并
12.2 数据清洗
12.3 数据标准化
12.4 数据转换
习题
13 使用scikit-learn构建模型
13.1 分类与回归问题简介
13.2 使用scikit-learn转换器处理数据
13.3 监督学习算法——分类与回归
习题
14 基于机器学习方法的中小微企业信贷决策
14.1 问题的提出
14.2 数据预处理
14.3 问题 1 的求解
习题
15 抗乳腺癌候选药物的优化建模与分析
15.1 数据预处理
15.2 变量的筛选
15.3 ERα生物活性的定量预测
习题
参考文献

FAQ

Does this book require readers to have a programming background?

This book is positioned as a zero-foundation introduction, starting from Python installation and basic syntax, and requires no prior programming experience. However, some logical thinking ability and patience will be helpful for learning.

Are the code examples and data analysis cases in the book outdated?

The book is written based on relatively stable Python 3.x versions and mainstream data analysis libraries, and its core syntax, data structures, and analysis logic have long-term validity. Although specific library versions may be updated, the basic principles and operation methods in the book are common and do not affect its learning value.

What level can I reach after finishing this book?

After finishing the book, you will be able to skillfully use Python for basic data reading, cleaning, organizing, analysis, and visualization, independently complete common data processing tasks, and lay a solid foundation for learning more advanced data science or machine learning content.

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About the author

赵胜利 Author

Zhao Shengli is an author about whom little public information is available; his nationality, dates of birth and death, educational background, and professional career have not been confirmed and await further verification. According to the library's records, Zhao Shengli has one work, Python Programming Basics and Data Analysis, which is classified under programming and design. In terms of subject matter, the book addresses introductory programming and practical data analysis, roughly covering the fundamentals of the Python language and methods of data processing, and serves as an introductory text that connects programming skills with hands-on data analysis. Its writing is expected to emphasize the laying out of basic knowledge and the explanation of operational methods so as to lower the barrier for beginners. Because background information on the author is scarce, it is currently difficult to judge the full character of his writing style or the actual impact of the work, and only limited inferences can be drawn from the title and classification; more complete materials may be added later.

苏理云 Author

Public information about Su Liyun is currently limited; it is known only that the author has one work, Python Programming Basics and Data Analysis, commonly classified under programming and design. The title suggests a book aimed at beginners that combines the basic syntax of the Python language with practical applications in data analysis, making it a programming title that balances introductory teaching with hands-on practice. The author's approach appears to connect the fundamentals of programming with ways of thinking about data processing, likely covering basic concepts such as variables, control structures, and functions, and extending to common methods for organizing, computing, and analyzing data with Python. Because more biographical information is unavailable, the author's education, profession, and research direction cannot be confirmed, but this work reflects a writing focus on introductory programming education and entry-level data analysis, and is suitable for readers who wish to learn Python systematically and enter the field of data analysis.

邱世芳 Author

Qiu Shifang: publicly available information is limited. What can be confirmed is that one work is held in the library under this name, Python Programming Basics and Data Analysis, commonly classified under programming and design. Judging from the subject matter, the author focuses on introductory Python and applied data analysis, addressing readers who wish to use programming to handle data and combining basic syntax with hands-on data analysis practice. Because the library holds no further personal details, the author's nationality, dates of birth and death, education, and professional background cannot be determined, and no other works are available for comparison, making it difficult to summarize a consistent writing style or overall influence. On the basis of the existing work alone, the author can be regarded as one of the writers in the field of introductory programming and data analysis books. This profile is based on library records and may be revised if further information becomes available.