Search results for ""pragmatic bookshelf""

Don't engineer by coincidence-design it like you mean it! Filled with practical techniques, Design It! is the perfect introduction to software architecture for programmers who are ready to grow their design skills. Lead your team as a software architect, ask the right stakeholders the right questions, explore design options, and help your team implement a system that promotes the right -ilities. Share your design decisions, facilitate collaborative design workshops that are fast, effective, and fun-and develop more awesome software! With dozens of design methods, examples, and practical know-how, Design It! shows you how to become a software architect. Walk through the core concepts every architect must know, discover how to apply them, and learn a variety of skills that will make you a better programmer, leader, and designer. Uncover the big ideas behind software architecture and gain confidence working on projects big and small. Plan, design, implement, and evaluate software architectures and collaborate with your team, stakeholders, and other architects. Identify the right stakeholders and understand their needs, dig for architecturally significant requirements, write amazing quality attribute scenarios, and make confident decisions. Choose technologies based on their architectural impact, facilitate architecture-centric design workshops, and evaluate architectures using lightweight, effective methods. Write lean architecture descriptions people love to read. Run an architecture design studio, implement the architecture you've designed, and grow your team's architectural knowledge. Good design requires good communication. Talk about your software architecture with stakeholders using whiteboards, documents, and code, and apply architecture-focused design methods in your day-to-day practice. Hands-on exercises, real-world scenarios, and practical team-based decision-making tools will get everyone on board and give you the experience you need to become a confident software architect.

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When you write software, you need to be at the top of your game. Great programmers practice to keep their skills sharp. Get sharp and stay sharp with more than fifty practice exercises rooted in real-world scenarios. If you're a new programmer, these challenges will help you learn what you need to break into the field, and if you're a seasoned pro, you can use these exercises to learn that hot new language for your next gig. One of the best ways to learn a programming language is to use it to solve problems. That's what this book is all about. Instead of questions rooted in theory, this book presents problems you'll encounter in everyday software development. These problems are designed for people learning their first programming language, and they also provide a learning path for experienced developers to learn a new language quickly. Start with simple input and output programs. Do some currency conversion and figure out how many months it takes to pay off a credit card. Calculate blood alcohol content and determine if it's safe to drive.Replace words in files and filter records, and use web services to display the weather, store data, and show how many people are in space right now. At the end you'll tackle a few larger programs that will help you bring everything together. Each problem includes constraints and challenges to push you further, but it's up to you to come up with the solutions. And next year, when you want to learn a new programming language or style of programming (perhaps OOP vs. functional), you can work through this book again, using new approaches to solve familiar problems. What You Need: You need access to a computer, a programming language reference, and the programming language you want to use.

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Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.

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Your domain is rich and interconnected, and your API should be too. Upgrade your web API to GraphQL, leveraging its flexible queries to empower your users, and its declarative structure to simplify your code. Absinthe is the GraphQL toolkit for Elixir, a functional programming language designed to enable massive concurrency atop robust application architectures. Written by the creators of Absinthe, this book will help you take full advantage of these two groundbreaking technologies. Build your own flexible, high-performance APIs using step-by-step guidance and expert advice you won't find anywhere else. GraphQL is a new way of structuring and building web services, and the result is transformational. Find out how to offer a more tailored, cohesive experience to your users, easily aggregate data from different data sources, and improve your back end's maintainability with Absinthe's declarative approach to defining how your API works. Build a GraphQL-based API from scratch using Absinthe, starting from core principles. Learn the type system and how to expand your schema to suit your application's needs. Discover a growing ecosystem of tools and utilities to understand, debug, and document your API. Take it to production, but do it safely with solid best practices in mind. Find out how complexity analysis and persisted queries can let you support your users flexibly, but responsibly too. Along the way, discover how Elixir makes all the difference for a high performance, fault-tolerant API. Use asynchronous and batching execution, or write your own custom add-ons to extend Absinthe. Go live with subscriptions, delivering data over websockets on top of Elixir (and Erlang/OTP's) famous solid performance and real-time capabilities. Transform your applications with the powerful combination of Elixir and GraphQL, using Absinthe. What You Need: To follow along with the book, you should have Erlang/OTP 19+ and Elixir 1.4+ installed. The book will guide you through setting up a new Phoenix application using Absinthe.

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The next step in the evolution of user interfaces is here. Chatbots let your users interact with your service in their own natural language. Use free and open source tools along with Ruby to build creative, useful, and unexpected interactions for users. Take advantage of the Lita framework's step-by-step implementation strategy to simplify bot development and testing. From novices to experts, chatbots are an area in which everyone can participate. Exercise your creativity by creating chatbot skills for communicating, information, and fun. Developers of all skill levels can craft user experiences that are natural, easy to use, and most of all, fun. Build chatbots using free, open source tools and launch them to popular chat platforms like Slack and Amazon's Alexa. Use the Ruby programming language and the Lita bot framework to unlock fun and powerful chat abilities such as sending text messages and emails, creating new meme images, driving a robot around the room, and talking out loud on a home speaker. Use frameworks available in Ruby and Node.js to get started quickly. Create simple chatbot skills that respond quickly to basic requests. Chain skills together for more complex interactions. Take advantage of test-driven development techniques to build your bots with confidence. Coordinate tasks with colleagues via bot. Connect with external APIs to provide users with data they need. Extract data information from web pages when an API isn't available. Expand your bot's reach with SMS and e-mail messaging. Deploy a chatbot to a host so users can interact with it on their schedule. Build a more responsive, easy-to-use interface for your users today. What You Need: You don't need much to get started with chatbots. A Mac or Linux computer with a recent version of Ruby is recommended. Windows users can keep up with a free virtual machine running Linux. You'll deploy your chatbots for free (or at least cheaply) on cloud hosting platforms like Heroku and Digital Ocean.

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Reactive programming is revolutionary. It makes asynchronous programming clean, intuitive, and robust. Use RxJS 5 to write complex programs in a simple way, and master the Observable: a powerful data type that substitutes callbacks and promises. Think about your programs as streams of data that change and adapt to produce what you want. Manage real-world concurrency and write complex flows of events in your applications with ease. Take advantage of Schedulers to make asynchronous testing easier. The code in this new edition is completely updated for RxJS 5 and ES6. Create concurrent applications with ease using RxJS 5, a powerful event composition library. Real-world JavaScript applications require you to master asynchronous programming, and chances are that you'll spend more time coordinating asynchronous events than writing actual functionality. This book introduces concepts and tools that will greatly simplify the process of writing asynchronous programs. Find out about Observables, a unifying data type that simplifies concurrent code and eases the pain of callbacks. Learn how Schedulers change the concept of time itself, making asynchronous testing sane again. Find real-world examples for the browser and Node.js along the way: how about a real-time earthquake visualization in 20 lines of code, or a frantic shoot-'em-up space videogame? You'll also use Cycle.js - a modern, reactive, web framework - to make a new breed of web applications. By the end of the book, you'll know how to think in a reactive way, and to use RxJS 5 to build complex programs and create amazing reactive user interfaces. You'll also understand how to integrate it with your existing projects and use it with the frameworks you already know. All the code in this new edition has been thoroughly revised and updated for RxJS 5, ES6, and Cycle.js Unified.

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You've heard that quantum computing is going to change the world. Now you can check it out for yourself. Learn how quantum computing works, and write programs that run on the IBM Q quantum computer, one of the world's first functioning quantum computers. Learn a simple way to apply quantum mechanics to computer programming. Create algorithms to solve intractable problems for classical computers, and discover how to explore the entire problem space at once to determine the optimal solution. Get your hands on the future of computing today. Quantum computing overhauls computer science. Problems such as designing life-saving drugs and super-large logistics problems that have been difficult or impossible for classical computers to handle can now be solved in moments. Quantum computing makes it possible to explore all possible solutions simultaneously and determine those that work, instead of iterating through each possibility sequentially. Work with quantum computers directly, instead of talking about them theoretically. Work with qubits, the fundamental elements of quantum computing. Discover what makes them different from classical bits. Model complex problems with logic gates specific to quantum computing. Learn how quantum mechanics offers ways to write programs that explore all solutions simultaneously. Create quantum circuits to solve problems that classical computers struggle with. Dive into quantum optimization and cryptography. Use the IBM Q quantum computer to both simulate quantum effects, and actually run your programs on a real quantum machine. Get a head start on the technology that will drive computer science into the future. What You Need: Access to the IBM quantum computer, via any internet connection

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Your team is stressed; priorities are unclear. You're not sure what your teammates are working on, and management isn't helping. If your team is struggling with any of these symptoms, these four case studies will guide you to project success. See how Kanban was used to significantly improve time to market and to create a shared focus across marketing, IT, and operations. Each case study comes with illustrations of the Kanban board and diagrams and graphs to help you see behind the scenes. Learn a Lean approach by seeing how Kanban made a difference in four real-world situations. You'll explore how four different teams used Kanban to make paradigm-changing improvements in software development. These teams were struggling with overwork, unclear priorities, and lack of direction. As you discover what worked for them, you'll understand how to make significant changes in real situations.The four case studies in this book explain how to: * Improve the full value chain by using Enterprise Kanban * Boost engagement, teamwork, and flow in change management and operations * Save a derailing project with Kanban * Help an office team outside IT keep up with growth using Kanban What seems easy in theory can become tangled in practice. Discover why "improving IT" can make you miss your biggest improvement opportunities, and why you should focus on fixing quality and front-end operations before IT. Discover how to keep long-term focus and improve across department borders while dealing with everyday challenges. Find out what happened when using Kanban to find better ways to do work in a well-established company, including running multi-team development without a project office. You'll inspire your team and engage management to make it easier to develop better products. What You Need: This is a case study book, so there are no software requirements. The book covers the relevant bits of theory before presenting the case studies.

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Put the power of Haskell to work in your programs, learning from an engineer who uses Haskell daily to get practical work done efficiently. Leverage powerful features like Monad Transformers and Type Families to build useful applications. Realize the benefits of a pure functional language, like protecting your code from side effects. Manage concurrent processes fearlessly. Apply functional techniques to working with databases and building RESTful services. Don't get bogged down in theory, but learn to employ advanced programming concepts to solve real-world problems. Don't just learn the syntax, but dive deeply into Haskell as you build efficient, well-tested programs. Haskell is a pure functional programming language with a rich ecosystem of tools and libraries. Designed to push the boundaries of programming, it offers unparalleled power for building reliable and maintainable systems. But to unleash that power, you need a guide. Effective Haskell is that guide. Written by an engineer who understands how to apply Haskell to the real world and uses it daily to get practical work done, it is your ticket to Haskell mastery. Gain deep understanding of how Haskell deals with IO and the outside world by writing a complete Haskell application that does several different kinds of IO. Reinforce your learnings with practice exercises in every chapter. Write stable and performant code using Haskell's type system, code that is easier to grow and refactor. Leverage the power of pure functional programming to improve collaboration, make concurrency safe and easy, and make large code bases manageable. Implement type-safe web services, write generative tests, design strongly typed embedded domain-specific languages, and build applications that exploit parallelism and concurrency without fear of deadlocks and race conditions. Create and deploy cloud-native Haskell applications. Master the performance characteristics of functional applications to make them run faster and use less memory. Write Haskell programs that solve real-world business problems. What You Need: Intel based Mac, M1 Macs, Linux PC, or Windows with WSL2 ghcup (http://www. Haskell.org/ghcup/) An active internet connection will be required for some projects.

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Make your good Python code even better by following proven and effective pythonic programming tips. Avoid logical errors that usually go undetected by Python linters and code formatters, such as frequent data look-ups in long lists, improper use of local and global variables, and mishandled user input. Discover rare language features, like rational numbers, set comprehensions, counters, and pickling, that may boost your productivity. Discover how to apply general programming patterns, including caching, in your Python code. Become a better-than-average Python programmer, and develop self-documented, maintainable, easy-to-understand programs that are fast to run and hard to break. Python is one of the most popular and rapidly growing modern programming languages. With more than 200 standard libraries and even more third-party libraries, it reaches into the software development areas as diverse as artificial intelligence, bioinformatics, natural language processing, and computer vision. Find out how to improve your understanding of the spirit of the language by using one hundred pythonic tips to make your code safer, faster, and better documented. This programming style manual is a quick reference of helpful hints and a random source of inspiration. Choose the suitable data structures for searching and sorting jobs and become aware of how a wrong choice may cause your application to be completely ineffective. Understand global and local variables, class and instance attributes, and information-hiding techniques. Create functions with flexible interfaces. Manage intermediate computation results by caching them in files and memory to improve performance and reliability. Polish your documentation skills to make your code easy for other programmers to understand. As a bonus, discover Easter eggs cleverly planted in the standard library by its developers. Polish, secure, and speed-up your Python applications, and make them easier to maintain by following pythonic programming tips. What You Need: You will need a Python interpreter (ideally, version 3.4 or above) and the standard Python library that usually comes with the interpreter.

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Test applications, packages, and libraries large and small with pytest, Python's most powerful testing framework. pytest helps you write tests quickly and keep them readable and maintainable. In this fully revised edition, explore pytest's superpowers - simple asserts, fixtures, parametrization, markers, and plugins - while creating simple tests and test suites against a small database application. Using a robust yet simple fixture model, it's just as easy to write small tests with pytest as it is to scale up to complex functional testing. This book shows you how. pytest is undeniably the best choice for testing Python projects. It's a full-featured, flexible, and extensible testing framework. pytest's fixture model allows you to share test data and setup procedures across multiple layers of tests. The pytest framework gives you powerful features such as assert rewriting, parametrization, markers, plugins, parallel test execution, and clear test failure reporting - with no boilerplate code. With simple step-by-step instructions and sample code, this book gets you up to speed quickly on this easy-to-learn yet powerful tool. Write short, maintainable tests that elegantly express what you're testing. Speed up test times by distributing tests across multiple processors and running tests in parallel. Use Python's builtin assert statements instead of awkward assert helper functions to make your tests more readable. Move setup code out of tests and into fixtures to separate setup failures from test failures. Test error conditions and corner cases with expected exception testing, and use one test to run many test cases with parameterized testing. Extend pytest with plugins, connect it to continuous integration systems, and use it in tandem with tox, mock, coverage, and even existing unittest tests. Write simple, maintainable tests quickly with pytest. What You Need: The examples in this book were written using Python 3.9 and pytest 6. pytest 6 supports Python 3.5 and above.

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Are you working on a codebase where cost overruns, death marches, and heroic fights with legacy code monsters are the norm? Battle these adversaries with novel ways to identify and prioritize technical debt, based on behavioral data from how developers work with code. And that's just for starters. Because good code involves social design, as well as technical design, you can find surprising dependencies between people and code to resolve coordination bottlenecks among teams. Best of all, the techniques build on behavioral data that you already have: your version-control system. Join the fight for better code! Use statistics and data science to uncover both problematic code and the behavioral patterns of the developers who build your software. This combination gives you insights you can't get from the code alone. Use these insights to prioritize refactoring needs, measure their effect, find implicit dependencies between different modules, and automatically create knowledge maps of your system based on actual code contributions. In a radical, much-needed change from common practice, guide organizational decisions with objective data by measuring how well your development teams align with the software architecture. Discover a comprehensive set of practical analysis techniques based on version-control data, where each point is illustrated with a case study from a real-world codebase. Because the techniques are language neutral, you can apply them to your own code no matter what programming language you use. Guide organizational decisions with objective data by measuring how well your development teams align with the software architecture. Apply research findings from social psychology to software development, ensuring you get the tools you need to coach your organization towards better code. If you're an experienced programmer, software architect, or technical manager, you'll get a new perspective that will change how you work with code. What You Need: You don't have to install anything to follow along in the book. TThe case studies in the book use well-known open source projects hosted on GitHub. You'll use CodeScene, a free software analysis tool for open source projects, for the case studies. We also discuss alternative tooling options where they exist.

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