pandas庫的函數令人眼花缭亂,Complex problems in reality inevitably make people at a loss.如果你剛開始使用pandas,It is normal to encounter errors,Even if the repair is done correctly,The next time you encounter a similar problem, you may have forgotten the previous solution,This situation sounds frustrating.Therefore it is recommended that you read it《pandas數據處理與分析》,本書前 3 Parts are divided into“1+4+4”的模塊結構, 即“pandas 基礎”+ “4 類 pandas 操作”+“4 類 pandas 數據”,The logical relationship between functions is summarized in each module,thereby showing The macro system of data processing.除了數據處理,Also analyze the data,So on top of the previous structure,Readers should 該掌握 3 個問題的解決方案,即“怎麼分析”“怎麼處理”“怎麼加速”,這對應“數據觀測”“特 Levy project”和“性能優化”這 3 個知識模塊.
Data processing and analysis are practical tasks,Readers need to consolidate what they have learned with some high-quality exercises.因此, 本 The book comes with a certain number of exercises,These exercises can help readers understand、Reinforce and extend what is presented in the book.
《pandas數據處理與分析》(耿遠昊)【摘要 書評 試讀】- 京東圖書item.jd.com/13268767.html正在上傳…重新上傳取消
實戰式pandas教程,梳理pandas中常用的函數,Combine a lot of code to explain theoretical knowledge,Show the macro system of data processing,Provide high-quality practice,幫助讀者理解、Reinforce and expand what you have learned.
基於PandasThe official recommended Chinese tutorialJoyful Pandas,實戰式Pandas教程“Panda Books”.
本書共包含13章,第一部分介紹NumPy和pandas的基本內容;第二部分介紹pandas庫中的4類操作,包括索引、分組、Deformation and connection;The third part is based on the introductionpandas庫的4類數據,包括缺失數據、文本數據、分類data and time series data,and introduce this4class data processing method;The fourth section introduces the data observations、特Levy projectand performance optimization related content.This book features a wealth of exercises,The last section of each chapter is an exercise,At the same time, each chapter contains many instant exercises（練一練）.These exercises allow readers to put their broad understanding of data science into practice.
pandas是Python數據科學生態中一個核心的第三方庫.使用pandas,我們能夠快捷、高效地解決現實中各類與數據相關的問題.本書全面講解了基於pandas的數據處理與分析技術,理論與實踐相結合,是學習pandas的優秀教程.
——張日權 華東師范大學Dean of the School of Statistics, Faculty of Economics and Management,教授、博士生導師
Python作為數字經濟時代最受歡迎的編程語言之一,正成為廣大有志於投身數據科學領域的青年學子必學的技術.“Joyful Pandas”是Datawhale社區的開源項目,也是pandas官方目前唯一推薦的中文教程,本書在該教程的基礎上進一步完善,強化理論與實踐的結合,對Python初學者和進階者均有裨益.
—— 陳海強 廈門大學王亞南經濟研究院教授、博士生導師
Data analysis capabilities are gradually becoming 數字化The basic skills that learners should have in the wave of development.本書分為“基礎知識”“4類操作”“4類數據”和“進階實戰”四大部分,結合簡潔易懂的代碼示例,涵蓋pandas的所有核心操作與特性,非常適合數據分析人員自學.
——黃鹂強 浙江大學Professor of Data Science、博士生導師
This book does not require readers to have prior knowledge of data science or data analysis,Just have the basics Python 語法知識.This book also applies to some pandas Basic readers who want to systematically learn data processing and analysis methods.For already right pandas Readers with some knowledge of data science,Reading this book can also play a role in consolidating and expanding knowledge.
The book is divided into basics（第 1 章～第 2 章）、4 類操作（第 3 章～第 6 章）、4 類數據（第 7 章～第 10 章）和進階實戰（第 11 章～第 13 章）4 個部分.
第一部分包含 Python 基礎、NumPy 基礎和 pandas 基礎.其中,Python Basic retrospective derivation、匿 Concepts and applications of named functions and packaged functions;NumPy The base contains the common ones數組操作, 如構造、變形、切片、廣播
mechanism and commonly used functions.pandas Basic include file reading and writing、基本數據結構、Commonly used basic functions and windows 對象.
The second part introduces the index、分組、Transform and connect this 4 類操作.其中,第 3 chapter covers單級索引、多級索引 and common indexing methods;第 4 Chapter introduces the basic concepts of grouping patterns and their objects、聚合函數的使用方法、Transformation function and Usage of filter functions,以及跨列Group related content;第 5 Chapter discusses the deformation and other deformation methods of the aspect table;第 6 章 Basic concepts involving relational connections、Common relational join functions and others連接函數等.
The third section introduces missing data、文本數據、Categorical data and time series data 4 類數據.其中,第 7 Zhang She and four operations for missing data—統計、刪除、填充、插值,以及對 Nullable A detailed explanation of the type;第 8 章 涵蓋 str 對象、正則表達式基礎、文本處理的5 類操作—拆分、合並、匹配、替換、提取,以及 常用字符串函數;第 9 Zhang She及 cat 對象、Ordinal categories as well as interval categories;第 10 chapter covers timestamps、時間差、 Contents of date offset and time series operations.
The fourth section contains data observations、Content for feature engineering and performance optimization.第 11 This chapter introduces the basic methods and numbers of visualization According to the general idea of observation.第 12 章介紹Single feature construction、Common methods for multi-feature construction and feature selection.第 13 章介紹 pandas 代碼編寫的注意事項、Multi-process-based acceleration method、基於 Cython The acceleration method and based on Numba 的加速方法.
Python data structure (Lecture 1 Introduction - Lecture 2 algorithm analysis)
Python data structure Languag
[Wu Enda] machine learning homework -ex3data1/weights- neural network forward (Python)
One . Preface &n
