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

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

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

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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

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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

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

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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.