Learn Python Data Analysis the Easy Way, Compared with Excel

张俊红Publishing House of Electronics Industry2019

Rating 3.8
Word count
369 words
Chapters
15
Series
Getting Started as a Data Analyst Series
ISBN
9787121357930

Tags

Computing Software TutorialsArtificial Intelligence Data AnalysisProgramming LanguageWorkplace EfficiencyBeginner TutorialBasic GrammarPractical GuidePractice SectionPopular Narrative

Summary

This is an introductory Python data analysis book for office workers and data newcomers, whose core approach is to use Excel habits to understand Python code implementation, thereby lowering the learning threshold. The book is organized according to the standard data analysis workflow: getting familiar with tools, defining the purpose, acquiring data, getting familiar with data, processing data, analyzing data, drawing conclusions, validating conclusions, and presenting conclusions. The introductory section first explains what data analysis is, why it is done, common analysis metrics, and the complete workflow, and explains the respective roles of Excel and Python; the practice section starts with Python environment installation and Jupyter Notebook usage, covering basic syntax, strings, lists, dictionaries, tuples, conditions and loops, and functions, then moves into Pandas Series and DataFrame, with each chapter corresponding to Excel operations, including importing xlsx, csv, txt, and sql data, handling missing and duplicate values, outlier detection, type conversion and index setting, row and column selection, sorting, replacement, deletion, counting, binning, long-wide table conversion, aggregation operations, time series processing, grouping and pivot tables, horizontal and vertical concatenation of multiple tables, exporting results to xlsx and csv, and Matplotlib visualization from canvas coordinate systems to line charts, bar charts, scatter plots, box plots, heatmaps, dual axes, and other common charts. The advanced section presents report automation, automated email sending, and analysis cases in supermarket and banking scenarios, and supplements NumPy array knowledge. The book is about 369,000 Chinese characters, suitable as an operation manual for the data analysis workflow and as a desk reference for quick lookup.

Editor's review

学 Python 数据分析，很多人卡在第一步：代码看得懂，却不知道它在 Excel 里对应哪一步操作。这本书正好解决了这个难题：它用数据人早已熟悉的 Excel 操作当参照，同一步操作分别给出 Excel 的做法和 Pandas 的写法，让读者用熟悉的方式过渡到代码。全书按真实的分析流程组织，从导入数据、清洗预处理、筛选与数值操作，到分组透视、多表拼接、结果导出、画图呈现，顺序清楚、前后衔接紧密。它不讲形式化理论，而是直接回答某一步该怎么做，几乎每一章都能对应到日常工作中的实际动作。入门篇铺垫概念，实践篇是主体，进阶篇用报表自动化、自动收邮件和超市、银行两个行业案例，把前面的章节串起来。作者张俊红长期从事数据分析，熟悉 Python、SQL、Excel，书里的例子偏向真实办公场景。对 Excel 熟练、又想让重复工作自动化的职场人，这本书的性价比很高。提醒一句：本书出版于 2019 年，部分库的版本与写法已有更新，练习时注意对照官方文档。

Who it's for

A good fit for

Not a good fit for

Reading tips

  1. 对照阅读：遇到一个 Python 写法，先回想自己平时用 Excel 怎么操作，再理解代码逻辑。
  2. 动手复现：务必安装 Jupyter Notebook，把每章示例数据亲手跑一遍，理解每行代码的作用。
  3. 勤查手册：本书出版于 2019 年，Pandas 和 Matplotlib 版本更新较快，遇到报错优先查官方文档。
  4. 学以致用：读完第 14 章后，挑一份自己工作中的 Excel 报表，试着用 Python 把它重做一次。

About the publisher

Publishing House of Electronics Industry China · Founded in 1982

The Publishing House of Electronics Industry is a comprehensive publisher distinguished by its focus on electronic information and related fields. Judging from the more than 3,000 titles in its catalogue, its publishing covers programming and design, artificial intelligence, computer science, finance and economics, photography, and social sciences, attending both to cutting-edge technology and to general readers. Representative titles include Factor Investing: Methods and Practice, x86 Assembly Language: From Real Mode to Protected Mode, The RISC-V Reader, Artificial Intelligence: Speech Recognition, Understanding and Practice, Phased Array Radar Technology, Radar Target Characteristics, and 100 Go Mistakes and How to Avoid Them, most of which centre on technical methods and engineering practice. Its authors include Shi Chuan, Li Zhong, Wang Xiaobo, David Patterson, Andrew Waterman, and Yu Zihao, among others from China and abroad. Owing to limited public information, no further publishing features are elaborated here.

Contents

  1. Chapter 1 Fundamentals of Data Analysis (purposes of analysis, common metrics, standard workflow, and the roles of Excel and Python)
  2. Chapter 2 Getting Acquainted with the Pot: Python Basics (environment setup, Jupyter Notebook, basic syntax, and data structures)
  3. Chapter 3 Pandas Data Structures (Series and DataFrame)
  4. Chapter 4 Preparing the Ingredients: Acquiring Data Sources (importing xlsx, csv, txt, and sql files, and previewing data)
  5. Chapter 5 Washing the Rice: Data Preprocessing (missing values, duplicates, outliers, type conversion, and index setting)
  6. Chapter 6 Choosing the Dishes: Data Selection (selecting columns, selecting rows, and selecting rows and columns together)
  7. Chapter 7 Cutting and Prepping: Numerical Operations (replacing, sorting, ranking, dropping, counting, binning, and reshaping between long and wide formats)
  8. Chapter 8 Start Cooking: Data Computation (arithmetic, comparison, aggregation, and correlation operations)
  9. Chapter 9 The Cooking Timer: Time Series
  10. Chapter 10 Sorting the Dishes: Data Grouping / Pivot Tables
  11. Chapter 11 Fruit Platter: Combining Multiple Tables (horizontal and vertical concatenation)
  12. Chapter 12 Plating and Serving: Exporting Results (exporting to xlsx, csv, and multiple sheets)
  13. Chapter 13 Arranging the Dishes: Data Visualization (canvas and coordinate systems, common charts, and chart formatting)
  14. Chapter 14 Typical Data Analysis Cases (report automation, automated email sending, supermarket and banking analysis)
  15. Chapter 15 NumPy Arrays

FAQ

完全没有编程基础能看懂这本书吗？ 可以。作者刻意用 Excel 操作作参照，不从代码语法讲起；但前提是你要熟悉 Excel 的常用操作，本书更偏向让懂 Excel 的人过渡到 Python，而不是从零讲起。
这本书用的是哪个 Python 库？ 主体是 Pandas，涵盖数据导入、清洗、筛选、分组透视、多表拼接与导出；图表部分使用 Matplotlib，进阶篇第 15 章补充 NumPy 数组基础，不涉及 scikit-learn 等建模库。
书中的代码和现在的库版本能兼容吗？ 本书 2019 年出版，Pandas 与 Matplotlib 后续版本有接口变化，部分写法可能报弃用警告，需要按官方文档微调。
这本书有配套数据和代码吗？ 书中案例以示例数据配合讲解为主，实际练习时建议自备同结构的小数据集，跟着章节一步步操作，效果最好。
读完这本书能达到什么水平？ 大致能用 Pandas 完成一份中等复杂度的数据分析：取数、清洗、汇总、透视、导出与可视化，并可尝试用脚本替代重复的 Excel 报表工作。

This site is an index: you start “Search all sources” in your browser, and the entries it lists all point to third-party sites; please judge their availability yourself.

About the author

张俊红 Author

Zhang Junhong is an author about whom limited public information is available. One work by this author is included in the library: Contrast with Excel: Easily Learn Python Report Automation. Judging from the title and its common classification, the book falls under software learning and is aimed at readers who wish to use their existing spreadsheet experience to move into Python-based report automation. It lowers the learning threshold through comparison and emphasizes practicality and hands-on operation. The author's writing stays close to everyday office scenarios, connecting programming knowledge with report-processing needs and helping readers understand the similarities, differences, and links between the two tools. As no reliable records of birth and death dates, nationality, education, or professional experience are available, this introduction makes no speculation and only briefly describes the subject positioning and writing approach of the work. Further public information may be added later.