Clean Python
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Clean Code In Python
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Author : Mariano Anaya
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-29
Clean Code In Python written by Mariano Anaya and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-29 with Computers categories.
Getting the most out of Python to improve your codebase Key Features Save maintenance costs by learning to fix your legacy codebase Learn the principles and techniques of refactoring Apply microservices to your legacy systems by implementing practical techniques Book Description 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. What you will learn Set up tools to effectively work in a development environment Explore how the magic methods of Python can help us write better code Examine the traits of Python to create advanced object-oriented design Understand removal of duplicated code using decorators and descriptors Effectively refactor code with the help of unit tests Learn to implement the SOLID principles in Python Who this book is for This book will appeal to team leads, software architects and senior software engineers who would like to work on their legacy systems to save cost and improve efficiency. A strong understanding of Programming is assumed.
Clean Python
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Author : Sunil Kapil
language : en
Publisher: Apress
Release Date : 2019-05-21
Clean Python written by Sunil Kapil and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-21 with Computers categories.
Discover the right way to code in Python. This book provides the tips and techniques you need to produce cleaner, error-free, and eloquent Python projects. Your journey to better code starts with understanding the importance of formatting and documenting your code for maximum readability, utilizing built-in data structures and Python dictionary for improved maintainability, and working with modules and meta-classes to effectively organize your code. You will then dive deep into the new features of the Python language and learn how to effectively utilize them. Next, you will decode key concepts such as asynchronous programming, Python data types, type hinting, and path handling. Learn tips to debug and conduct unit and integration tests in your Python code to ensure your code is ready for production. The final leg of your learning journey equips you with essential tools for version management, managing live code, and intelligent code completion. After reading and using this book, you will be proficient in writing clean Python code and successfully apply these principles to your own Python projects. What You’ll Learn Use the right expressions and statements in your Python code Create and assess Python Dictionary Work with advanced data structures in Python Write better modules, classes, functions, and metaclasses Start writing asynchronous Python immediately Discover new features in Python Who This Book Is For Readers with a basic Python programming knowledge who want to improve their Python programming skills by learning right way to code in Python.
Clean Code In Python
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Author : Mariano Anaya
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-01-06
Clean Code In Python written by Mariano Anaya and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-06 with Computers categories.
Tackle inefficiencies and errors the Pythonic way Key Features Enhance your coding skills using the new features introduced in Python 3.9 Implement the refactoring techniques and SOLID principles in Python Apply microservices to your legacy systems by implementing practical techniques Book Description 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. What you will learn Set up a productive development environment by leveraging automatic tools Leverage the magic methods in Python to write better code, abstracting complexity away and encapsulating details Create advanced object-oriented designs using unique features of Python, such as descriptors Eliminate duplicated code by creating powerful abstractions using software engineering principles of object-oriented design Create Python-specific solutions using decorators and descriptors Refactor code effectively with the help of unit tests Build the foundations for solid architecture with a clean code base as its cornerstone Who this book is for This book is designed to benefit new as well as experienced programmers. It will appeal to team leads, software architects and senior software engineers who would like to write Pythonic code to save on costs and improve efficiency. The book assumes that you have a strong understanding of programming
Beyond The Basic Stuff With Python
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Author : Al Sweigart
language : en
Publisher: No Starch Press
Release Date : 2020-12-16
Beyond The Basic Stuff With Python written by Al Sweigart and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-16 with Computers categories.
BRIDGE THE GAP BETWEEN NOVICE AND PROFESSIONAL You've completed a basic Python programming tutorial or finished Al Sweigart's bestseller, Automate the Boring Stuff with Python. What's the next step toward becoming a capable, confident software developer? Welcome to Beyond the Basic Stuff with Python. More than a mere collection of advanced syntax and masterful tips for writing clean code, you'll learn how to advance your Python programming skills by using the command line and other professional tools like code formatters, type checkers, linters, and version control. Sweigart takes you through best practices for setting up your development environment, naming variables, and improving readability, then tackles documentation, organization and performance measurement, as well as object-oriented design and the Big-O algorithm analysis commonly used in coding interviews. The skills you learn will boost your ability to program--not just in Python but in any language. You'll learn: Coding style, and how to use Python's Black auto-formatting tool for cleaner code Common sources of bugs, and how to detect them with static analyzers How to structure the files in your code projects with the Cookiecutter template tool Functional programming techniques like lambda and higher-order functions How to profile the speed of your code with Python's built-in timeit and cProfile modules The computer science behind Big-O algorithm analysis How to make your comments and docstrings informative, and how often to write them How to create classes in object-oriented programming, and why they're used to organize code Toward the end of the book you'll read a detailed source-code breakdown of two classic command-line games, the Tower of Hanoi (a logic puzzle) and Four-in-a-Row (a two-player tile-dropping game), and a breakdown of how their code follows the book's best practices. You'll test your skills by implementing the program yourself. Of course, no single book can make you a professional software developer. But Beyond the Basic Stuff with Python will get you further down that path and make you a better programmer, as you learn to write readable code that's easy to debug and perfectly Pythonic Requirements: Covers Python 3.6 and higher
Robust Python
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Author : Patrick Viafore
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-07-12
Robust Python written by Patrick Viafore and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-12 with Computers categories.
Does it seem like your Python projects are getting bigger and bigger? Are you feeling the pain as your codebase expands and gets tougher to debug and maintain? Python is an easy language to learn and use, but that also means systems can quickly grow beyond comprehension. Thankfully, Python has features to help developers overcome maintainability woes. In this practical book, author Patrick Viafore shows you how to use Python's type system to the max. You'll look at user-defined types, such as classes and enums, and Python's type hinting system. You'll also learn how to make Python extensible and how to use a comprehensive testing strategy as a safety net. With these tips and techniques, you'll write clearer and more maintainable code. Learn why types are essential in modern development ecosystems Understand how type choices such as classes, dictionaries, and enums reflect specific intents Make Python extensible for the future without adding bloat Use popular Python tools to increase the safety and robustness of your codebase Evaluate current code to detect common maintainability gotchas Build a safety net around your codebase with linters and tests
Python Data Cleaning Cookbook
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Author : Michael Walker
language : en
Publisher:
Release Date : 2020-12-11
Python Data Cleaning Cookbook written by Michael Walker and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-11 with Computers categories.
Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricks Key features Get well-versed with various data cleaning techniques to reveal key insights Manipulate data of different complexities to shape them into the right form as per your business needs Clean, monitor, and validate large data volumes to diagnose problems before moving on to data analysis Book Description Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data. By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it. What you will learn Find out how to read and analyze data from a variety of sources Produce summaries of the attributes of data frames, columns, and rows Filter data and select columns of interest that satisfy given criteria Address messy data issues, including working with dates and missing values Improve your productivity in Python pandas by using method chaining Use visualizations to gain additional insights and identify potential data issues Enhance your ability to learn what is going on in your data Build user-defined functions and classes to automate data cleaning Who this book is for This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book.
Clean Architecture With Python
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Author : Sam Keen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-06-20
Clean Architecture With Python written by Sam Keen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-20 with Computers categories.
Future-proof your Python projects by creating flexible code that adapts to changing requirements with the help of this hands-on guide to achieving Clean Architecture Key Features Learn Clean Architecture through a series of real-world, code-centric examples and exercises Optimize system componentization, significantly reducing maintenance burden and overall complexity Apply Clean Architecture concepts confidently to new Python projects and legacy code refactoring Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the rapidly evolving tech industry, software applications struggle to keep pace with changing business needs, leaving developers grappling with complex codebases that resist change, ultimately reducing productivity and increasing technical debt. Clean Architecture with Python offers a powerful approach to address these challenges. Drawing from his extensive experience architecting cloud-native systems, Sam Keen helps you transform complex architectural challenges into digestible, implementable solutions. This book teaches essential principles for effective development, emphasizing the Pythonic implementation of Clean Architecture. Through practical examples, you'll learn how to create modular, loosely coupled systems that are easy to understand, modify, and extend. The book covers key concepts such as the Dependency Rule, separation of concerns, and domain modeling, all tailored for Python development. By the end of this book, you'll be able to apply Clean Architecture principles effectively in your Python projects. Whether you're building new systems or managing existing ones, you'll have the skills to create more maintainable and adaptable applications. This approach will enhance your ability to respond to changing requirements, setting you up for long-term success in your development career.What you will learn Apply Clean Architecture principles idiomatically in Python Implement domain-driven design to isolate core business logic Apply SOLID principles in a Pythonic context to improve code quality Structure projects for maintainability and ease of modification Develop testing techniques for cleanly architected Python applications Refactor legacy Python code to adhere to Clean Architecture principles Design scalable APIs and web applications using Clean Architecture Who this book is for If you're a Python developer struggling with maintaining and extending complex codebases, this book is for you. It's ideal for intermediate developers looking to enhance their architectural skills as well as senior developers seeking to formalize their knowledge of Clean Architecture in Python. While beginners can benefit, prior experience with Python and object-oriented programming is recommended.
Data Visualization With Python And Javascript
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Author : Kyran Dale
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-06-30
Data Visualization With Python And Javascript written by Kyran Dale and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-30 with Computers categories.
Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations. As a working example, throughout the book Dale walks you through transforming Wikipedia’s table-based list of Nobel Prize winners into an interactive visualization. You’ll examine steps along the entire toolchain, from scraping, cleaning, exploring, and delivering data to building the visualization with JavaScript’s D3 library. If you’re ready to create your own web-based data visualizations—and know either Python or JavaScript— this is the book for you. Learn how to manipulate data with Python Understand the commonalities between Python and JavaScript Extract information from websites by using Python’s web-scraping tools, BeautifulSoup and Scrapy Clean and explore data with Python’s Pandas, Matplotlib, and Numpy libraries Serve data and create RESTful web APIs with Python’s Flask framework Create engaging, interactive web visualizations with JavaScript’s D3 library
The Hitchhiker S Guide To Python
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Author : Kenneth Reitz
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-08-30
The Hitchhiker S Guide To Python written by Kenneth Reitz and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-30 with Computers categories.
The Hitchhiker's Guide to Python takes the journeyman Pythonista to true expertise. More than any other language, Python was created with the philosophy of simplicity and parsimony. Now 25 years old, Python has become the primary or secondary language (after SQL) for many business users. With popularity comes diversityâ??and possibly dilution. This guide, collaboratively written by over a hundred members of the Python community, describes best practices currently used by package and application developers. Unlike other books for this audience, The Hitchhikerâ??s Guide is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist.
Cleaning Data For Effective Data Science
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Author : David Mertz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-03-31
Cleaning Data For Effective Data Science written by David Mertz and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-31 with Mathematics categories.
Think about your data intelligently and ask the right questions Key FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description 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. What you will learnIngest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structuresUnderstand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and BashApply useful rules and heuristics for assessing data quality and detecting bias, like Benford’s law and the 68-95-99.7 ruleIdentify and handle unreliable data and outliers, examining z-score and other statistical propertiesImpute sensible values into missing data and use sampling to fix imbalancesUse dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your dataWork carefully with time series data, performing de-trending and interpolationWho this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.