Effective Python

Python被譽為最易上手的程式語言，但你真的是用Python的腦袋寫Python嗎？ 身為Python的開發者，想要「寫得更好」，還想知道「為什麼這樣寫比較好？」 《Effective Python 第三版》延續前兩版廣受好評的風格，集結125個經過實戰驗證的開發技巧，全面涵蓋Python 3.11最新語法與標準函式庫。從基礎語言機制、函式與類別設計，到協同程式、型別提示與效能優化，每一項建議都清楚說明「為什麼這樣寫比較好」，幫助你避開常見雷區、寫出更簡潔、可維護、具備Python風格的程式碼。 🔹 條目式設計，查找方便、精練實用 🔹 專為Python 3.11打造，掌握語言最新演進 🔹 每個條目皆含範例、反例與最佳實踐 🔹 作者為Google資深工程師，實戰經驗豐富 不僅告訴你「怎麼寫」，更像是經驗豐富的前輩手把手教你「為什麼這樣寫更好」，帶你從語言使用者晉升為真正精通Python的開發者！ ----------------------------------------------------------------------- 精通Python程式設計技術，掌握125個具體可行的最佳實務做法，寫出更加高效、易讀、易維護的程式碼 Python是一門功能多元且強大的語言，但想真正發揮它的潛力，光是熟悉語法還不夠。Effective Python中文版 第三版，是你掌握Python獨特優勢、避開隱藏陷阱的全方位指南。本書奠基於廣受好評的第二版，將最佳實務做法從90項擴增至125項，這些都是撰寫高品質 Python 程式碼的關鍵基石。 作者Brett Slatkin汲取其多年在Google的深厚實務經驗，為初階與資深的Python開發者提供了清晰、簡潔且實用的建議。書中每一個做法都深入剖析了「Pythonic」的程式設計之道，幫助你理解如何寫出不僅正確、有效，更能兼具優雅與易維護性的程式碼。無論你是在建構Web應用程式、分析資料、撰寫自動化指令稿，或是訓練AI模型，本書都將賦予你必要的技能，讓你能運用Python創造非凡的影響力。 主要特點包括 ■ 內容升級：新增35個全新做法，擴增至125條具體可行的準則。 ■ 對應新版：涵蓋至Python3.13新版功能的語法與實務趨勢。 ■ 新增章節：剖析如何建置兼具穩健性與高效能的程式。 ■ 進階主題：深入探討如何

本書是Effective Python的第2版，基於Python 3進行了全面升級。本書分為10章，包含90個條目，形式簡潔、表述明確。每個條目都基於對Python的獨到見解，告訴讀者如何高效地編寫Python程序。與第1版相比，第2版只關注Python 3，而不再兼顧 Python 2。根據Python語言引入的新特性，以及Python開發者所形成的新經驗，對第1版中的某些思路與解決方案進行了修訂，以更好地發揮Python的優勢。本書刪除了過時的知識點，並添加了對Python新特性的一些介紹。新版中增加了31個條目，並專門設立了幾章來強調列表和字典、推導和生成器、元類和屬性、穩定性和性能，以及測試和調試等重要主題。

●本書可以幫你掌握真正的Pythonic編程方式，令你能夠完全發揮出Python語言的強大功能，並寫出健壯而高效的代碼。Scott Meyers在暢銷書Effective C++中開創了一種以使用場景為主導的精練教學方式，本書作者Brett Slatkin就以這種方式匯聚了90條實踐原則、開發技巧和便捷方案，並以實用的代碼範例來解釋它們。●Slatkin根據自己在Google公司多年開發Pytho

本書作者BrettSlatkin以使用場景為主導的精練教學方式，彙聚了59條優秀的實踐原則、開發技巧和便捷方案，並以實用的代碼範例來解釋它們。作者根據自己在Google公司多年開發Python基礎架構所積累的經驗，揭示了Python語言中一些鮮為人知的微妙特性，並給出了能夠改善代碼功能及運行效率的習慣用法。通過本書，你能夠瞭解到解決關鍵程式設計任務所用的最佳方式，並學會編寫易於理解、便於維護且利於

It's easy to start writing code with Python: that's why the language is so immensely popular. However, Python has unique strengths, charms, and expressivity that can be hard to grasp at first -- as we

本書不是要講述 Python 的基礎程式設計，而是要幫你掌握 Python 獨特的優勢和魅力。書中總結了 59個 Python 程式設計的優秀實踐、貼士和捷徑，並用真實代碼示例進行了解釋。全書共分 8 章，第 1 章講述Python 的風格思想，介紹了 Python 中常見問題的推薦解決方案；第 2 章講述如何使用 Python 函數來闡明意圖、提升再使用性，並減少錯誤；第 3 章介紹如何使用類和

內容簡介:「Slatkin 的《Effective Python》中每個主題都是自成一體的一堂課，包含對應該主題的程式碼，這讓你能夠隨意翻閱此書。其中的主題易於瀏覽，並可依讀者需要的順序來研讀。我會向 Python 的學員推薦《Effective Python》這本書，它以相當精簡的方式包含了非常廣泛的主題，為具有相當程度的 Python 程式設計師提供了主流的建議做法。」 —Brandon Rh

Fully-updated, second edition of this worldwide bestseller with over 100,000 copies sold. When it comes to creating powerful and effective hacking tools, Python is the language of choice for most secu

An effective guide to geographic information systems and remote sensing analysis using Python 3About This BookConstruct applications for GIS development by exploiting PythonThis focuses on built-in Py

Learn to use powerful Python libraries for effective data processing and analysisAbout This BookLearn the basic processing steps in data analysis and how to use Python in this area through supported p

Generate effective results in a variety of visually appealing charts using the plotting packages in PythonAbout This BookExplore various tools and their strengths while building meaningful representat

Python is the high-level language of choice for hackers and software security analysts because it makes it easy to write powerful and effective security tools. A follow-up to the perennial best-seller

An effective guide to geographic information systems and remote sensing analysis using Python 3About This BookConstruct applications for GIS development by exploiting PythonThis focuses on built-in Py

"數據科學項目日益增多，每個項目在提出原型到生產的過程中都需要可靠的基礎設施。使用《Effective數據科學基礎設施》介紹的一些新技術和新工具，你將能建立一個適用於各類組織(無論是初創企業還是大型企業)的基礎設施堆棧。《Effective數據科學基礎設施》可幫助你建立數據流程和項目工作流，為你開發項目帶來強大動力。《Effective數據科學基礎設施》呈現Netflix數據操作的**進工具和概念，並在此基礎上介紹一種可定制的基於雲的模型開發和MLOps方法，可輕鬆適應公司的特定需求。當團隊把數據科學和機器學習應用於廣泛的業務問題時，這些實用的數據流程將更高效地生成更完美的結果。主要內容● 在雲中處理計算和編排● 將基於雲的工具耦合到一個內聚的數據科學環境中● 使用Metaflow、AWS和Python數據生態系統開發可複製的數據科學項目● 構建需要大型數據集和模型，需要數據科學家團隊參與的複雜應用程序"

The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you're new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful dat

Learn a simpler and more effective way to analyze data and predict outcomes with PythonMachine Learning in Python shows you how to successfully analyze data using only two core machine learning algori

The book is designed for upper level under-graduates, graduate students and researchers in various STEM disciplines interested in performing efficient and cost effective image acquisition and processi

All social and policy researchers need to synthesize data into a visual representation. Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analysed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book's website.

All social and policy researchers need to synthesize data into a visual representation. Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analysed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book's website.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.