Python Packages introduces Python packaging at an introductory and practical level that’s suitable for those with no previous packaging experience. Despite this, the text builds up to advanced topics such as automated testing, creating documentation, versioning and updating a package, and implementing continuous integration and deployment. Covering the entire Python packaging life cycle, this essential guide takes readers from package creation all the way to effective maintenance and updating. Python Packages focuses on the use of current and best-practice packaging tools and services like poetry, cookiecutter, pytest, sphinx, GitHub, and GitHub Actions. Features: The book’s source code is available online as a GitHub repository where it is collaborated on, automatically tested, and built in real time as changes are made; demonstrating the use of good reproducible and clear project workflows. Covers not just the process of creating a package, but also how to document it, test it, publish it to the Python Package Index (PyPI), and how to properly version and update it. All concepts in the book are demonstrated using examples. Readers can follow along, creating their own Python packages using the reproducible code provided in the text. Focuses on a modern approach to Python packaging with emphasis on automating and streamlining the packaging process using new and emerging tools such as poetry and GitHub Actions.

Unlock the World of Coding with "Introduction to Programming and Problem Solving Using Python" ' This book serves as your friendly guide to the world of programming, using Python as the key to unlock its vast potential. With a hands-on approach and real-world examples, you'll discover the beauty of Python's simplicity and versatility, whether you're a complete beginner or coming from another programming background. Learn to think like a programmer as you tackle common coding challenges and build your problem-solving skills step by step. From mastering the fundamentals of Python syntax to building a logical thought process required for coding, this book empowers you to write efficient, elegant code that solves real-world problems. Salient features of the book: · Suitable for the beginners as well as intermediate level programmers · Numerous interesting programming examples are provided with due explanation · End of the chapter exercises for additional practice · Programs are based on Python Version 3.0 and above · Special chapter on small projects in Python, prepares you for the professional level of coding Join us on this exciting journey and watch as the world of coding unfolds before your eyes.

Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

What can you do with the Raspberry Pi, the affordable computer the size of a credit card? All sorts of things! If you're learning how to program--or looking to build new electronic projects, this hands-on guide will show you just how valuable this flexible little platform can be. Updated to include coverage of the Raspberry Pi Model B+, Getting Started with Raspberry Pi takes you step-by-step through many fun and educational possibilities. Take advantage of several preloaded programming languages. Use the Raspberry Pi with Arduino. Create Internet-connected projects. Play with multimedia. With Raspberry Pi, you can do all of this and more. In Getting Started with Raspberry Pi, you’ll: Get acquainted with hardware features on the Pi's board Learn enough Linux to move around the operating system Start programming in Python and Scratch Draw graphics, play sounds, and handle mouse events with Pygame Use the Pi's input and output pins to do some hardware hacking Discover how Arduino and the Raspberry Pi can work together Create your own Pi-based web server with Python Work with the Raspberry Pi Camera Module and USB webcams

Make the best of your test suites by using cutting-edge software architecture patterns in Python Key FeaturesLearn how to create scalable and maintainable applicationsBuild a web system for micro messaging using concepts in the bookUse profiling to find bottlenecks and improve the speed of the systemBook Description Developing large-scale systems that continuously grow in scale and complexity requires a thorough understanding of how software projects should be implemented. Software developers, architects, and technical management teams rely on high-level software design patterns such as microservices architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD) to make their work easier. This book covers these proven architecture design patterns with a forward-looking approach to help Python developers manage application complexity—and get the most value out of their test suites. Starting with the initial stages of design, you will learn about the main blocks and mental flow to use at the start of a project. The book covers various architectural patterns like microservices, web services, and event-driven structures and how to choose the one best suited to your project. Establishing a foundation of required concepts, you will progress into development, debugging, and testing to produce high-quality code that is ready for deployment. You will learn about ongoing operations on how to continue the task after the system is deployed to end users, as the software development lifecycle is never finished. By the end of this Python book, you will have developed "architectural thinking": a different way of approaching software design, including making changes to ongoing systems. What you will learnThink like an architect, analyzing software architecture patternsExplore API design, data storage, and data representation methodsInvestigate the nuances of common architectural structuresUtilize and interoperate elements of patterns such as microservicesImplement test-driven development to perform quality code testingRecognize chunks of code that can be restructured as packagesMaintain backward compatibility and deploy iterative changesWho this book is for This book will help software developers and architects understand the structure of large complex systems and adopt architectural patterns that are scalable. Examples in the book are implemented in Python so a fair grasp of basic Python concepts is expected. Proficiency in any programming languages such as Java or JavaScript is sufficient.

Microcontrollers, like the RP2040 at the heart of Raspberry Pi Pico, are computers stripped back to their bare essentials. You don't use monitors or keyboards with them, but program them over USB to take their input from (and send their output to) their input/output (IO) pins. Using these programmable connections, you can light lights, make noises, send text to screens, and much more. In Get Started with MicroPython on Raspberry Pi Pico, you will learn how to use the beginner-friendly language MicroPython to write programs and connect up hardware to make your Raspberry Pi Pico interact with the world around it. Using these skills, you can create your own electro-mechanical projects, whether for fun or to make your life easier. This book shows you how to: Get started with Raspberry Pi Pico Work with various electronic components Create your own programmable electronic contraptions Work with Programming Input and Output (PIO) for low level, timing-critical projects Learn the Raspberry Pi Pico pinouts for hooking up components Use the I2C and SPI protocols to connect to components By the end of the book, you'll know how to create your own programmable electronic contraptions. What you do with them is up to you.