Python Data Science Handbook

《Python編程基礎與應用》共分為三部分。第壹部分是Python語言基礎，介紹Python的基礎編程、數據結構、結構化編程、函數以及模塊和包等內容；第二部分是Python編程進階，包括面向對象編程、數據分析與可視化以及數據持久化等內容；第三部分是使用PyQt進行界面開發。《Python編程基礎與應用》既可以作為高等院校計算機軟件相關專業的教材，也可以作為計算機專業人員、經濟/金融領域人員的自學或參考用書。

《Python量化金融與人工智能》內容包括：量化金融投資基礎及Python下載、安裝與啟動，Python編程基礎，Python金融數據存取，Python工具庫NumPy數組與矩陣計算，Python工具庫SciPy優化與統計分析，Python工具庫Pandas數據物件及數據分析，Python描述統計，Python參數估計及其應用，Python參數假設檢驗，Python相關分析與回歸分析，Python多重共線性處理，Python異方差處理，Python自相關處理，Python金融時間序列分析的日期處理，Python金融時間序列的自相關性與平穩性，Python金融時間序列分析的ARIMA模型，Python金融時間序列分析的ARCH與GARCH模型，Python資產組合的收益率與風險，Python-optimize工具優化資產組合均值方差模型，Python在有無風險資產的均值方差模型中的應用，Python在資本資產定價模型中的應用，貝塔對衝策略，量化選股策略分析，量化擇時策略分析，量化選股與量化擇時組合策略分析，金融大數據量化投資統計套利的協整配對交易策略，基於Python環境的配對交易策略，人工智能機器學習量化金融策略等。 《Python量化金融與人工智能》內容新穎、全面，實用性強，融理論、方法、應用於一體，是一部供金融學、金融工程、投資學、保險學、經濟學、財政學、統計學、應用統計、數量經濟學、管理科學與工程、計算機應用技術、應用數學、計算數學、概率統計、財務管理、會計學、工商管理等專業的本科高年級學生與研究生使用的參考書。

《Python從入門到精通（第2版）》從初學者角度出發，通過通俗易懂的語言、豐富多彩的實例，詳細介紹了使用Python進行程序開發應該掌握的各方面技術。全書共分23章，包括初識Python、Python語言基礎、運算符與表達式、流程控制語句、列表和元組、字典和集合、字符串、Python中使用正則表達式、函數、面向物件程序設計、模塊、異常處理及程序調試、文件及目錄操作

本书侧重于Python程序开发过程中重点和难点的理解，结合具有典型性的程序案例，帮助读者掌握Python语言。全书由Python编程基础、Python高级编程、Python应用技术和Python实战演练4个部分组。

The first ever interdisciplinary handbook in the field, this vital resource offers wide-ranging analysis of health research regulation. The chapters confront gaps between documented law and research in practice, and draw on legal, ethical and social theories about what counts as robust research regulation to make recommendations for future directions. The Handbook provides an account and analysis of current regulatory tools - such as consent to participation in research and the anonymization of data to protection participants' privacy - as well as commentary on the roles of the actors and stakeholders who are involved in human health research and its regulation. Drawing on a range of international examples of research using patient data, tissue and other human materials, the collective contribution of the volume is to explore current challenges in delivering good medical research for the public good and to provide insights on how to design better regulatory approaches. This title is al

Deploy Python creatively and effectively through this collection of short coding projects carefullly curated by best-selling author and Python expert Al Sweigart. This curated collection of simple Pyt

Experimental syntax is an area that is rapidly growing as linguistic research becomes increasingly focused on replicable language data, in both fieldwork and laboratory environments. The first of its kind, this handbook provides an in-depth overview of current issues and trends in this field, with contributions from leading international scholars. It pays special attention to sentence acceptability experiments, outlining current best practices in conducting tests, and pointing out promising new avenues for future research. Separate sections review research results from the past 20 years, covering specific syntactic phenomena and language types. The handbook also outlines other common psycholinguistic and neurolinguistic methods for studying syntax, comparing and contrasting them with acceptability experiments, and giving useful perspectives on the interplay between theoretical and experimental linguistics. Providing an up-to-date reference on this exciting field, it is essential readin

本書將幫助讀者系統掌握編寫高效、優雅的Python代碼的方法，快速成長為一位高水平的Python開發工程師。 作者是Python編程領域的資深開發工程師，有多年的Python開發經驗，本書得到了行業裡15位技術專家的高度評價。 為了幫助讀者編寫出高效的Python代碼，本書不僅從源代碼的角度系統地講解了Python的語法，讓讀者能知其然並知其所以然，

本書共13章，主要內容涵蓋Python語法及資料分析方法。第1章主要介紹資料分析的概念，使讀者有一個大致的印象，並簡單介紹本書頻繁使用的Python的5個協力廠商庫。第2章主要做一些準備工作，手把手帶讀者搭建Python環境，包括Python 3.7.6的安裝和pip的安裝。第3章介紹Python程序設計基礎。第4章到第7章介紹使用Python進行簡單資料分析的基礎庫，包

Python是目前熱門的開發語言之一， ESRI公司已經將Python作為ArcGIS產品的主要二次開發語言。在ArcGIS開發中使用Python語言，既可以高效地調取ArcGIS地理數據處理的功能，又可以便捷地使用眾多Python的開源資源。本書結合大量地理實際應用代碼和數據，重點介紹了Python的基礎知識、ArcGIS中Python的使用方法、地圖文檔和圖層的訪問與管理、空間數據訪問與操作、矢量數據和柵格數據分析、地圖制圖、自定義工具、插件製作等內容。 本書可供地理學、生態學等相關專業從事地理數據處理的人員學習使用，從而快速掌握ArcGIS Python編程方法。

Bullshit isn't what it used to be. Now, two science professors give us the tools to dismantle misinformation and think clearly in a world of fake news and bad data. "A modern classic . . . a straight-talking survival guide to the mean streets of a dying democracy and a global pandemic."--Wired Misinformation, disinformation, and fake news abound and it's increasingly difficult to know what's true. Our media environment has become hyperpartisan. Science is conducted by press release. Startup culture elevates bullshit to high art. We are fairly well equipped to spot the sort of old-school bullshit that is based in fancy rhetoric and weasel words, but most of us don't feel qualified to challenge the avalanche of new-school bullshit presented in the language of math, science, or statistics. In Calling Bullshit, Professors Carl Bergstrom and Jevin West give us a set of powerful tools to cut through the most intimidating data. You don't need a lot of technical expertise to call out problems

The best survey of cognitive linguistics available, this Handbook provides a thorough explanation of its rich methodology, key results, and interdisciplinary context. With in-depth coverage of the research questions, basic concepts, and various theoretical approaches, the Handbook addresses newly emerging subfields and shows their contribution to the discipline. The Handbook introduces fields of study that have become central to cognitive linguistics, such as conceptual mappings and construction grammar. It explains all the main areas of linguistic analysis traditionally expected in a full linguistics framework, and includes fields of study such as language acquisition, sociolinguistics, diachronic studies, and corpus linguistics. Setting linguistic facts within the context of many other disciplines, the Handbook will be welcomed by researchers and students in a broad range of disciplines, including linguistics, cognitive science, neuroscience, gesture studies, computational linguistic

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques.Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/un

Economic activity is more globally integrated than ever before, but so is the scope of corporate misconduct. As more and more people across the world are affected by such malfeasance, the differences in legal redress have become increasingly visible. This transparency has resulted in a growing convergence towards an American model of robust private enforcement of the law, including the class-action lawsuit. This handbook brings together scholars from nearly two dozen countries to describe and assess the class-action procedure (or its equivalent) in their respective countries and, where possible, to offer empirical data on these systems. At the same time, the work presents a variety of multidisciplinary perspectives on class actions, from economics to philosophy, making this handbook an essential resource to academics, lawyers, and policymakers alike.

《Python機器學習》從實用的角度出發，整合Python語言基礎、數據分析與可視化、機器學習常用算法等知識。內容從*基本的Python編程基礎入手，由淺入深、循序漸進地講授NumPy庫和Matplotlib庫，以及複雜的機器學習基本理論和算法，並突出知識的實用性和可操作性。《Python機器學習》力求以淺顯的語言講解複雜的知識，以直觀的案例輔助讀者理解，並以圖表形式展示代碼和運行結果，配合習題鞏

《Python基礎及應用》完全為零基礎的初學者量身定做，配合大量實例介紹了Python的基本語法、編碼規範和一些編程思想。《Python基礎及應用》共分為兩部分，第1～6章為Python語言基礎，主要介紹Python的基本用法。第7～10章介紹一些Python的實際應用，第7章介紹了如何用Python進行GUI開發，第8章介紹了如何用Python開發網絡爬蟲，第9章介紹了如何用Python進行We

本書以MicroPython微控制器板“掌控板”為基礎，面向Python初學者講解Python編程基礎知識，並通過教學項目引導讀者學習Python嵌入式開發。 全書共8章，主要內容包括Python概要，Python基礎，字符串、列表和字典，內置函數與模塊，掌控板的顯示與輸出，音樂盒，按鍵操作與引腳控制，網絡應用等。

Python以其集命令式編程、函數式編程和面向對象編程於一身的特點，以及龐大的並正在急劇增長的模塊庫優勢，成為了程序設計界的新星。本書以Python 3.6為藍本，共分為6章，主要介紹Python編程的基本知識。第1章從模仿計算器入手，帶領讀者邁入Python的大門，介紹操作符、模塊、數據類型。第2章介紹Python命令式編程的關鍵內容：變量的概念、流程控制、函數、命名空間與變量作用域、Pytho

《用Python編程和實踐！深度學習教科書》是一本專門針對有一定編程經驗，但沒有Python和機器學習相關經驗的讀者編寫的參考書籍，目標是讓讀者能夠獨立編寫出機器學習相關的應用程序。書中首先介紹了機器學習和Python語言的基礎知識，然後對NumPy、Pandas、matplotlib等在Python中使用頻率較高的軟件庫進行講解；對數據可視化、lambda和map等Python語法、基於Data

本書既是一本針對所有層次讀者的Python編程教學書籍, 又是一本利用Python解決量化交易實際問題的專業書籍。本書共分為四部分。第一部分是Python語言基礎, 主要介紹Python的基礎編程、數據結構、結構化編程、函數以及模塊和包等內容, 這一部分算是Python的基本入門。第二部分是Python編程進階, 包括面向對象的編程、面向數據的分析與可視化以及數據持久化等內容。掌握了這一部分可以進

本書是一部系統論述Python 編程語言、OOP 編程思想以及函數式編程思想的立體化教程（含圖書、教學課件、源代碼與視頻教程）。全書共分為四篇：篇Python 語言基礎（ ~ 8 章），第二篇Python 編程進階（第9 ~17 章），第三篇Python 常用庫與框架（8 ~ 22 章），第四篇Python 項目實戰（第23 ~ 28 章）。主要內容包括：開篇綜述、開發環境搭建、個Python 程

全书共分为 １１ 章，主要内容有程序设计与算法、初识 Ｐｙｔｈｏｎ、Ｐｙｔｈｏｎ 语言基础、程序流程控制、 Ｐｙｔｈｏｎ 的组合数据结构、函数、面向对象程序设计、文件操作、正则表达式、Ｐｙｔｈｏｎ 与爬虫和数 据分析与绘图基础。本书既可作为应用型普通高等院校计算机、大数据等专业的计算机程序设计课程教材， 也可作为非计算机专业程序设计基础课程的教材。 本书配套编写了《Ｐｙｔｈｏｎ 程序设计实践》， 可用于本书所涉及章节的实践环节教学。

Many students find it daunting to move from studying environmental science, to designing and implementing their own research proposals. This book provides a practical introduction to help develop scientific thinking, aimed at undergraduate and new graduate students in the earth and environmental sciences. Students are guided through the steps of scientific thinking using published scientific literature and real environmental data. The book starts with advice on how to effectively read scientific papers, before outlining how to articulate testable questions and answer them using basic data analysis. The Mauna Loa CO2 dataset is used to demonstrate how to read metadata, prepare data, generate effective graphs and identify dominant cycles on various timescales. Practical, question-driven examples are explored to explain running averages, anomalies, correlations and simple linear models. The final chapter provides a framework for writing persuasive research proposals, making this an essent

Many students find it daunting to move from studying environmental science, to designing and implementing their own research proposals. This book provides a practical introduction to help develop scientific thinking, aimed at undergraduate and new graduate students in the earth and environmental sciences. Students are guided through the steps of scientific thinking using published scientific literature and real environmental data. The book starts with advice on how to effectively read scientific papers, before outlining how to articulate testable questions and answer them using basic data analysis. The Mauna Loa CO2 dataset is used to demonstrate how to read metadata, prepare data, generate effective graphs and identify dominant cycles on various timescales. Practical, question-driven examples are explored to explain running averages, anomalies, correlations and simple linear models. The final chapter provides a framework for writing persuasive research proposals, making this an essent

主要講解量化投資的思想和策略，並借助Python語言進行實戰。由三部分組成：首先，對Python編程語言的介紹，通過學習，讀者可以迅速掌握用Python語言處理資料的方法，靈活運用Python語言解決實際金融問題；其次，向讀者介紹量化投資的理論知識，主要講解量化投資所需的數量基礎與量化投資的類型等方面；最後，將以上兩部分內容結合起來，講述如何在Python語言中構建量化投資策略。

《Python科學計算及實踐》介紹如何用Python開發科學計算的應用程序，書中除了介紹數值計算外，還介紹了怎樣利用Python解決數值中的實際應用，帶領讀者領略利用Python解決實際問題的簡單、快捷等特性。《Python科學計算及實踐》共11章，具體內容主要有Python數值基礎、模型評估與概率統計、貝葉斯分類器、頻率與快速傅裡葉變換、線性回歸、多分類器系統、Scipy科學計算庫、統計分析、數值分析、數據可視化、數據處理等。《Python科學計算及實踐》可作為利用Python進行科學計算的廣大科研人員、學者、工程技術人員的參考書，也可作為高等院校相關專業的教材。

本書以Python3.7版本為編寫環境，分為上下兩篇，分別為：基礎篇―Python語言程序設計；進階篇―Python語言科學計算程序設計。本書的每章內容都包含Python程序知識要點、程序語句講解、例題、功能實現等，力求達到讀者能夠通過本書實現Python語言的入門和進階，並逐漸掌握程序設計思想的目標。

《精通Python(微課視頻版)》從初學者角度出發，使用通俗易懂的語言、豐富的實例，詳細介紹了Python語言的編程知識和應用技術。全書共4篇29章，基礎知識篇包括Python概述、Python語言基礎、運算符和表達式、語句和程序結構、列表和元組、字典和集合、字符串；進階提升篇包括正則表達式、函數、面向對象編程、模塊和包、異常處理和程序調試、文件和目錄操作；編程應用篇包括數據庫編程、Python界

一位拍博士，帶著一位小男生和一位小女生一起探索Python星球，完成一個又一個的任務。學習不枯燥，由一個連貫的故事入手，引人入勝，在不斷完成挑戰的過程中學會Python。從認識Python開始，由淺入深掌握Python的核心語法，進而運用Python體驗辦公自動化，在Excel中編寫九九乘法表，使用GUI製作軟件。書中融入了很多貼近生活和工作的案例並配備練習挑戰，“學習―應用―練習”交替讓學習更高

本書主要講解Python編程方法與技巧，共8個單元，32課，分為上、下兩冊。上冊四個單元分別為Python基礎語法、turtle畫圖、程序結構和百度AI；下冊四個單元分別為MicroPython、傳感控制、音樂娛樂、網絡爬蟲和串口通信。 本書的定位是讓學生零基礎學習Python編程。本書側重Python編程的實操性學習，並不強調Python知識系統性的講解。本書將Python的語法知識融入課程案例

《Python面試通關寶典》內容分為兩篇，第 1 篇是 Python 語言面試題，第 2 篇是算法面試題。Python 語言面試題涵蓋面試通用問題、Python 語法面試題、函數、類、模塊、文件管理、正則表達式以及 Python 語言綜合應用；算法面試題涵蓋排序、搜尋、字符串、數組、鏈表、二叉樹、堆棧、數學問題、貪婪算法、動態規劃算法以及綜合應用。

本書詳細介紹了Python 程序設計的基本環境、概念、方法和應用，特別是Python 在數據獲取、數據處理、數據展示等方面的應用。本書分為三部分：第一部分(第1～ 4 章)是Python 語言的基礎知識，包括數據與運算、語句與控制結構、組合數據類型；第二部分(第5 ～ 8 章)是Python 語言的高級部分，包括函數、常用的算法 、文件與異常、類與模塊；第三部分(第9 ～12 章)是Python

本書包括：經濟與金融計量分析緒論；描述性統計及其Python應用；參數估計及其Python應用；假設檢驗及其Python應用；相關分析與一元回歸分析及其Python應用；多元回歸分析及其Python應用；多重共線性及其Python應用；異方差及其Python應用；自相關及其Python應用；時間序列分析ARMA模型及其Python應用；廣義自回歸條件異方差模型(GARCH)及其Python應用；面

Want to kill it at your job interview in the tech industry? Want to win that coding competition? Learn all the algorithmic techniques and programming skills you need from two experienced coaches, problem setters, and jurors for coding competitions. The authors highlight the versatility of each algorithm by considering a variety of problems and show how to implement algorithms in simple and efficient code. Readers can expect to master 128 algorithms in Python and discover the right way to tackle a problem and quickly implement a solution of low complexity. Classic problems like Dijkstra's shortest path algorithm and Knuth-Morris-Pratt's string matching algorithm are featured alongside lesser known data structures like Fenwick trees and Knuth's dancing links. The book provides a framework to tackle algorithmic problem solving, including: Definition, Complexity, Applications, Algorithm, Key Information, Implementation, Variants, In Practice, and Problems. Python code included in the book

Experimental political science has changed. In two short decades, it evolved from an emergent method to an accepted method to a primary method. The challenge now is to ensure that experimentalists design sound studies and implement them in ways that illuminate cause and effect. Ethical boundaries must also be respected, results interpreted in a transparent manner, and data and research materials must be shared to ensure others can build on what has been learned. This book explores the application of new designs; the introduction of novel data sources, measurement approaches, and statistical methods; the use of experiments in more substantive domains; and discipline-wide discussions about the robustness, generalizability, and ethics of experiments in political science. By exploring these novel opportunities while also highlighting the concomitant challenges, this volume enables scholars and practitioners to conduct high-quality experiments that will make key contributions to knowledge.

Experimental political science has changed. In two short decades, it evolved from an emergent method to an accepted method to a primary method. The challenge now is to ensure that experimentalists design sound studies and implement them in ways that illuminate cause and effect. Ethical boundaries must also be respected, results interpreted in a transparent manner, and data and research materials must be shared to ensure others can build on what has been learned. This book explores the application of new designs; the introduction of novel data sources, measurement approaches, and statistical methods; the use of experiments in more substantive domains; and discipline-wide discussions about the robustness, generalizability, and ethics of experiments in political science. By exploring these novel opportunities while also highlighting the concomitant challenges, this volume enables scholars and practitioners to conduct high-quality experiments that will make key contributions to knowledge.

本書以Python程序設計為主線，從搭建Python運行環境出發，由淺入深、循序漸進地講述Python程序設計的基本概念和基本方法，通過大量精選的例題，講解程序設計思想和方法，從而培養學生程序設計能力。本書內容包括搭建Python運行環境、Python基礎語法、Python流程控制、函數與模塊、面向對象編程、組合數據類型、文件操作、數據庫操作、GUI編程、網絡爬蟲基礎、數據處理、數據可視化等。

本書系統講述了Python程序設計的基礎知識。全書共15章，內容包括：Python概述、Python基本語法、Python的基本流程控制、Python的四種典型序列結構、Python函數、Python文件和數據庫操作、面向對象程序設計、模塊和包、字符串操作與正則表達式的使用、錯誤及異常處理、GUI編程、網絡和多線程編程、基於Flask框架的Web編程、網絡爬蟲應用開發、數據分析與可視化基礎。為提升

Data stewards in any organization are the backbone of a successful data governance implementation because they do the work to make data trusted, dependable, and high quality. Since the publication of the first edition, there have been critical new developments in the field, such as integrating Data Stewardship into project management, handling Data Stewardship in large international companies, handling "big data" and Data Lakes, and a pivot in the overall thinking around the best way to align data stewardship to the data-moving from business/organizational function to data domain. Furthermore, the role of process in data stewardship is now recognized as key and needed to be covered.Data Stewardship, Second Edition provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on organizational/company structure, business functions, and data ownership. The book shows data managers how to gain support for a stewards

《Python 從基礎編程到數據分析》是一本同時介紹Python編程和基於Python的大數據分析圖書，既是一本Python入門書，又是一本Python數據分析專業書；既是Python基礎教程，又是Python視頻教程。 《Python從基礎編程到數據分析》共17章，首先介紹了Python的開發環境、代碼編寫風格及程序設計的常見錯誤類型；然後循序漸進地解說了Python編程的基礎語法和相關技巧，