Modern Python Cookbook - Second Edition: 133 recipes to develop flawless and expressive programs in Python 3.8
暫譯: 現代 Python 食譜 - 第二版：133 個食譜以開發無瑕且富表現力的 Python 3.8 程式

Lott, Steven F.

Modern Python Cookbook - Second Edition: 133 recipes to develop flawless and expressive programs in Python 3.8
  • 出版商: Packt Publishing
  • 出版日期: 2020-07-31
  • 售價: $1,698
  • 貴賓價: 9.5 折 $1,613
  • 語言: 英文
  • 頁數: 822
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 180020745X
  • ISBN-13: 9781800207455
  • 相關分類: Python、程式語言

    • 立即出貨 (庫存=1)

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商品描述

Complete recipes spread across 15 chapters to help you overcome commonly faced issues by Python for everybody across the globe. Each recipe takes a problem-solution approach to resolve for effective Python.

Key Features

  • Develop expressive and effective Python programs
  • Best practices and common idioms through carefully explained recipes
  • Discover new ways to apply Python for data-focused development
  • Make use of Python's optional type annotations

Book Description

Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand.

This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications.

The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it.

By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.

What you will learn

  • See the intricate details of the Python syntax and how to use it to your advantage
  • Improve your coding with Python readability through functions
  • Manipulate data effectively using built-in data structures
  • Get acquainted with advanced programming techniques in Python
  • Equip yourself with functional and statistical programming features
  • Write proper tests to be sure a program works as advertised
  • Integrate application software using Python

Who this book is for

The Python book is for web developers, programmers, enterprise programmers, engineers, and big data scientists. If you are a beginner, this book will get you started. If you are experienced, it will expand your knowledge base. A basic knowledge of programming would help.

商品描述(中文翻譯)

本書包含15個章節的完整食譜，幫助全球的Python使用者克服常見問題。每個食譜採用問題解決的方法，有效地解決Python相關的挑戰。

主要特色


  • 開發表達性和有效的Python程式

  • 透過精心解釋的食譜學習最佳實踐和常見慣用法

  • 探索將Python應用於數據導向開發的新方法

  • 利用Python的可選類型註解

書籍描述

Python是開發者、工程師、數據科學家和愛好者的首選語言。這是一種出色的語言，可以為您的應用程式提供卓越的速度、安全性和可擴展性。它可以用於簡單的腳本或複雜的網頁應用程式。通過將Python呈現為一系列簡單的食譜，本書讓您深入了解特定語言特性在特定上下文中的應用。具體的上下文有助於使語言或特定標準庫特性更易於理解。

本書包含133個針對最新版本Python 3.8的食譜。這些食譜將使每個人受益，從剛開始學習Python的初學者到專家。您不僅會學習Python程式設計概念，還會學會如何構建複雜的應用程式。

這些食譜將涵蓋所有與數據結構、物件導向程式設計、函數式程式設計和統計程式設計相關的必要Python概念。您將熟悉Python語法的細微差別，以及如何有效利用它。

在本書結束時，您將掌握測試、網路服務、配置和應用程式整合的技巧與竅門。您將具備創建具有靈活日誌記錄、強大配置、命令列選項、自動單元測試和良好文檔的應用程式的知識。

您將學到什麼


  • 了解Python語法的複雜細節以及如何利用它

  • 透過函數提高您的Python程式碼可讀性

  • 有效地使用內建數據結構操作數據

  • 熟悉Python中的高級程式設計技術

  • 掌握函數式和統計程式設計特性

  • 撰寫適當的測試以確保程式按預期運作

  • 使用Python整合應用程式軟體

本書適合誰

本書適合網頁開發者、程式設計師、企業程式設計師、工程師和大數據科學家。如果您是初學者，本書將幫助您入門。如果您有經驗，本書將擴展您的知識基礎。具備基本的程式設計知識將有所幫助。

作者簡介

Steven F. Lott has been programming since the 70s, when computers were large, expensive, and rare. As a contract software developer and architect, he has worked on hundreds of projects, from very small to very large. He's been using Python to solve business problems for almost 20 years.

He's currently leveraging Python to implement cloud management tools. His other titles with Packt Publishing include Python Essentials, Mastering Object-Oriented Python, Functional Python Programming, and Python for Secret Agents.

Steven is currently a technomad who lives in various places on the east coast of the U.S.

作者簡介(中文翻譯)

Steven F. Lott 自70年代以來一直在編程，那時候的電腦龐大、昂貴且稀有。作為一名合約軟體開發者和架構師，他參與了數百個項目，從非常小型到非常大型的都有。他已經使用 Python 解決商業問題近20年。

他目前正在利用 Python 實現雲端管理工具。他在 Packt Publishing 的其他著作包括《Python Essentials》、《Mastering Object-Oriented Python》、《Functional Python Programming》和《Python for Secret Agents》。

Steven 目前是一位技術遊牧者，居住在美國東海岸的各個地方。

目錄大綱

  1. Numbers, Strings, and Tuples
  2. Statements and Syntax
  3. Function Definitions
  4. Built-In Data Structures Part 1: Lists and Sets
  5. Built-In Data Structures Part 2: Dictionaries
  6. User Inputs and Outputs
  7. Basics of Classes and Objects
  8. More Advanced Class Design
  9. Functional Programming Features
  10. Input/Output, Physical Format, and Logical Layout
  11. Testing
  12. Web Services
  13. Application Integration: Configuration
  14. Application Integration: Combination
  15. Statistical Programming and Linear Regression

目錄大綱(中文翻譯)


  1. Numbers, Strings, and Tuples

  2. Statements and Syntax

  3. Function Definitions

  4. Built-In Data Structures Part 1: Lists and Sets

  5. Built-In Data Structures Part 2: Dictionaries

  6. User Inputs and Outputs

  7. Basics of Classes and Objects

  8. More Advanced Class Design

  9. Functional Programming Features

  10. Input/Output, Physical Format, and Logical Layout

  11. Testing

  12. Web Services

  13. Application Integration: Configuration

  14. Application Integration: Combination

  15. Statistical Programming and Linear Regression

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