Pandas Data Processing
Tags
Summary
Editor's review
Who it's for
A good fit for
- Beginners who want to systematically learn the Python Pandas library for data analysis
- Data workers with some Python foundation who need to improve data cleaning and processing efficiency
- Researchers or students looking for a clearly structured, case-rich Pandas reference book
- Data analysts who need to quickly look up common Pandas operations and techniques
Not a good fit for
- Absolute beginners with no knowledge of Python programming and no grasp of basic syntax
- Readers seeking advanced content beyond basic data processing, such as cutting-edge deep learning or complex statistical modeling
Reading tips
- Read the chapters in order and be sure to run the sample code to deepen your understanding
- Keep the book at hand as a desk reference and consult the relevant chapters for specific data processing tasks
- Focus on understanding the logic of data operations (such as grouping, merging, and pivoting) rather than memorizing function parameters, and apply the principles to your own projects
About the publisher
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.
Contents
- 版权信息
- 项目一 处理单个数据框
- 任务一 统计数据框中的数据
- 活动一创建学生成绩表
- 活动二统计各学科的得分信息
- 活动三查询总价最高的 10 个订单
- 任务二 查询和筛选数据框中的数据
- 活动一筛选出计算机老师
- 活动二查找联考专业成绩在 350 分以上的计算机专业男生
- 任务三 处理数据框中的字符串
- 活动一找出姓张的同学
- 活动二计算老师的上课时间
- 项目二 清洗数据
- 任务一 处理数据中的缺失值和重复值
- 活动一处理订单数据中的缺失值
- 活动二处理销售数据中的重复值
- 任务二 转换数据
- 活动一规范化学生考试成绩
- 活动二为优秀学生评奖
- 活动三规范化成绩表标题
- 项目三 分组统计数据
- 任务一 使用内置函数对数据进行分组统计
- 活动一为各类获奖同学准备奖金
- 活动二找出销量最少的产品
- 活动三统计各行业每年的上市公司数量
- 任务二 使用自定义函数对数据进行分组统计
- 活动一统计职位信息
- 活动二清洗网店销售数据
- 活动三统计各班的成绩结构
- 项目四 从多个数据框获取信息
- 任务一 合并多个数据框
- 活动一去掉停用词
- 活动二选取男性最喜欢的电影
- 任务二 拼接多个数据框
- 活动一统计各竞赛项目的人数
- 活动二统计各年新生儿的男女比例
- 项目五 改变数据框结构
- 任务一 展开和收折数据列
- 活动一按年级统计各参赛项目的男女生人数
- 活动二查询学生成绩
- 任务二 创建数据透视表和交叉表
- 活动一按班级分析成绩结构
- 活动二统计各地区不同风格的电影数量
- 项目六 绘制图表
- 任务一 绘制单个图表
- 活动一绘制各班专业课平均成绩柱状图
- 活动二绘制股票收益率折线图
- 任务二 绘制多个图表
- 活动一在不同子图绘制各班的成绩统计图
- 活动二按分数段绘制各班的人数占比
- 项目七 处理时间序列
- 任务一 对时间序列采样
- 活动一统计上证指数月度收益率
- 活动二按季度统计股票平均交易量和交易额
- 任务二 处理移动窗口
- 活动一制作股票的移动平均线
- 活动二制作股票价格与上证指数间的收益相关图
- 项目八 综合应用
- 任务一 使用数据透视表统计数据
- 活动一统计平均录取分数最高的前 10 所学校
- 活动二统计文理科历年平均录取分数和最高录取分数
- 任务二 使用分组统计数据
- 活动一统计各专业历年平均录取分数
- 活动二统计各地区最高录取分数的平均值
FAQ
What are the advantages of this book compared with the official documentation or other online tutorials?
How much mathematics or statistics background is needed to study this book?
Which version of Pandas is the code based on? Will it become outdated easily?
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About the author
Cen Yuanhong is a book author about whom limited public information is available. Two works by this author are currently known: Pandas Data Processing and Python Programming Design. Judging from the titles and the common classification of exam preparation, the writing is mainly aimed at programming study and test preparation. The content centers on the fundamentals of the Python language and the practical use of the data processing tool Pandas, making these books introductory and hands-on technical guides. Such works typically emphasize code examples and step-by-step explanations, helping readers practice chapter by chapter and meet the requirements of relevant exams or courses. Because the author's dates of birth and death, nationality, educational background, and professional experience are not clearly recorded, this introduction makes no speculation and merely summarizes the subject matter and purpose of the works based on in-house information, as a reference for readers choosing books.
Public information about Liu Xue is rather limited, and his nationality, dates of birth and death, and educational background cannot currently be confirmed. Judging from the five works held in the collection, his writing spans several fields, including finance and economics, management, software learning, and examinations, showing considerable interdisciplinary range. In finance, he offers systematic discussions of money and monetary policy, addressing core topics such as monetary circulation and central banking; in management, he focuses on the construction and future direction of platforms and ecosystems. He has also authored practical books on data processing and product photography, combining theoretical exposition with hands-on guidance. Overall, his works cover a wide range of subjects with an emphasis on practical application, making them suitable for learners and practitioners in the relevant fields.
Li Qiaoling: currently, the publicly available information is limited, and her full biography, nationality, and educational background cannot be confirmed. Judging from the two works included in the database, Pandas Data Processing and Python Programming Design, she is an author active in the field of computer and data analysis teaching, writing for beginners and exam-preparation readers, with her books commonly classified under examinations. Her works center on the fundamentals of the Python language and pandas data processing, emphasizing clear explanations of programming concepts and step-by-step operations, so that readers can practice and review in an orderly way; they are highly practical and useful as introductory guides. Although little is known about the author's own background, these two books offer a clear learning path for readers studying Python programming and data analysis, and have thus attracted attention and been adopted in related examination and self-study contexts.
Wu Chunling is an author with published works in the field of computer books. Public information about him is currently limited, and details such as his nationality, education, professional positions, and dates of birth and death are not clearly recorded. Judging from the four works included in the database, his writing focuses on programming design and general computer applications. Representative works include Object-Oriented Programming (Java Edition), Pandas Data Processing, Network Interconnection Technology (Theory Volume), and Python Programming. These subjects cover common teaching areas such as object-oriented programming, data analysis, network interconnection, and introductory general-purpose programming, and are roughly aimed at university students and beginning programmers. His works also fall under exam-related categories, suggesting the content may serve both course study and exam preparation needs. Overall, his writing style tends toward textbook-style systematic explanation, combining basic concepts with practical operation, making the books suitable as supplementary reading for computer-related courses.
