Wydawca: Packt Publishing
Clean Code in Python. Develop maintainable and efficient code - Second Edition
Mariano Anaya
Experienced professionals in every field face several instances of disorganization, poor readability, and testability due to unstructured code.With updated code and revised content aligned to the new features of Python 3.9, this second edition of Clean Code in Python will provide you with all the tools you need to overcome these obstacles and manage your projects successfully.The book begins by describing the basic elements of writing clean code and how it plays a key role in Python programming. You will learn about writing efficient and readable code using the Python standard library and best practices for software design.The book discusses object-oriented programming in Python and shows you how to use objects with descriptors and generators. It will also show you the design principles of software testing and how to resolve problems by implementing software design patterns in your code. In the concluding chapter, we break down a monolithic application into a microservices-based one starting from the code as the basis for a solid platform.By the end of this clean code book, you will be proficient in applying industry-approved coding practices to design clean, sustainable, and readable real-world Python code.
Clean Code in Python. Refactor your legacy code base
Mariano Anaya
Python is currently used in many different areas such as software construction, systems administration, and data processing. In all of these areas, experienced professionals can find examples of inefficiency, problems, and other perils, as a result of bad code. After reading this book, readers will understand these problems, and more importantly, how to correct them. The book begins by describing the basic elements of writing clean code and how it plays an important role in Python programming. You will learn about writing efficient and readable code using the Python standard library and best practices for software design. You will learn to implement the SOLID principles in Python and use decorators to improve your code. The book delves more deeply into object oriented programming in Python and shows you how to use objects with descriptors and generators. It will also show you the design principles of software testing and how to resolve software problems by implementing design patterns in your code. In the final chapter we break down a monolithic application to a microservice one, starting from the code as the basis for a solid platform. By the end of the book, you will be proficient in applying industry approved coding practices to design clean, sustainable and readable Python code.
Jason Alls
Traditionally associated with Windows desktop applications and game development, C# has expanded into web, cloud, and mobile development. However, despite its extensive coding features, professionals often encounter issues with efficiency, scalability, and maintainability due to poor code. Clean Code in C# guides you in identifying and resolving these problems using coding best practices.This book starts by comparing good and bad code to emphasize the importance of coding standards, principles, and methodologies. It then covers code reviews, unit testing, and test-driven development, and addresses cross-cutting concerns. As you advance through the chapters, you’ll discover programming best practices for objects, data structures, exception handling, and other aspects of writing C# computer programs. You’ll also explore API design and code quality enhancement tools, while studying examples of poor coding practices to understand what to avoid.By the end of this clean code book, you’ll have the developed the skills needed to apply industry-approved coding practices to write clean, readable, extendable, and maintainable C# code.
David Mertz
Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way.In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with.Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses.
Ludovico Serra
Clip Studio Paint is powerful art software that can help you create artistic work with its in-built material organizer, 3D integration, and group work features. It also provides other features that can speed up the workflow of illustrators, concept artists, and comic artists. With Clip Studio Paint by Example, you’ll learn how to use CSP effectively for a wide variety of artistic purposes.The book starts by helping you create the right workspace for concept art, illustration, and comics. You’ll create a brush, set up a canvas, and develop an auto-auction. Along with covering how to work with CS Modeler that comes bundled with CSP, this book shows you how to import and rig characters easily. You’ll then create reusable changeable scenes and a 3D human character in Blender before exploring concept art, illustrations, comics, and how to create your own portfolio. The book features a glossary with brief explanations of all the main CSP functions. The focus of the book is not on drawing or painting but on helping you enhance your artistic skills using Clip Studio Paint to create an impressive portfolio.By the end of this book, you’ll be able to use the impressive capabilities of CSP to create beautiful digital art in a productive way.
Clojure for Data Science. Statistics, big data, and machine learning for Clojure programmers
Henry Garner
The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a scripting language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs.Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility!You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models.Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future.