Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code.Explore Python’s major built-in object types such as numbers, lists, and dictionariesCreate and process objects with Python statements, and learn Python’s general syntax modelUse functions to avoid code redundancy and package code for reuseOrganize statements, functions, and other tools into larger components with modulesDive into classes: Python’s object-oriented programming tool for structuring codeWrite large programs with Python’s exception-handling model and development toolsLearn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing
If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master NLTK for natural language processing, then this Learning Path will do you a lot of good. Students of linguistics and semantic/sentiment analysis professionals will find it invaluable.What You Will LearnThe scope of natural language complexity and how they are processed by machinesClean and wrangle text using tokenization and chunking to help you process data betterTokenize text into sentences and sentences into wordsClassify text and perform sentiment analysisImplement string matching algorithms and normalization techniquesUnderstand and implement the concepts of information retrieval and text summarizationFind out how to implement various NLP tasks in PythonIn Detail
Natural Language Processing is a field of computational linguistics and artificial intelligence that deals with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. The number of human-computer interaction instances are increasing so it's becoming imperative that computers comprehend all major natural languages.
The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy.
The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods.
The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python.
This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products:NTLK essentials by Nitin HardeniyaPython 3 Text Processing with NLTK 3 Cookbook by Jacob PerkinsMastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti MathurStyle and approach
This comprehensive course creates a smooth learning path that teaches you how to get started with Natural Language Processing using Python and NLTK. You'll learn to create effective NLP and machine learning projects using Python and NLTK.
You'll learn language syntax and programming techniques in a clear and concise manner, with lots of examples that illustrate both correct usage and common idioms. Completely updated for version 3.x, Programming Python also delves into the language as a software development tool, with many code examples scaled specifically for that purpose.
Topics include:Quick Python tour: Build a simple demo that includes data representation, object-oriented programming, object persistence, GUIs, and website basicsSystem programming: Explore system interface tools and techniques for command-line scripting, processing files and folders, running programs in parallel, and moreGUI programming: Learn to use Python’s tkinter widget libraryInternet programming: Access client-side network protocols and email tools, use CGI scripts, and learn website implementation techniquesMore ways to apply Python: Implement data structures, parse text-based information, interface with databases, and extend and embed Python
Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra’s non-relational design, with special attention to data modeling. If you’re a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra’s speed and flexibility.Understand Cassandra’s distributed and decentralized structureUse the Cassandra Query Language (CQL) and cqlsh—the CQL shellCreate a working data model and compare it with an equivalent relational modelDevelop sample applications using client drivers for languages including Java, Python, and Node.jsExplore cluster topology and learn how nodes exchange dataMaintain a high level of performance in your clusterDeploy Cassandra on site, in the Cloud, or with DockerIntegrate Cassandra with Spark, Hadoop, Elasticsearch, Solr, and Lucene
Packed with examples and exercises, Natural Language Processing with Python will help you:
Extract information from unstructured text, either to guess the topic or identify "named entities"Analyze linguistic structure in text, including parsing and semantic analysisAccess popular linguistic databases, including WordNet and treebanksIntegrate techniques drawn from fields as diverse as linguistics and artificial intelligence
This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.
Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include:Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practicesFinancial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regressionSpecial topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies
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 Hitchhiker’s Guide is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist.
Introducing Integrating Excel and Access, the unique reference that shows you how to combine the strengths of Microsoft Excel with those of Microsoft Access. In particular, the book explains how the powerful analysis tools of Excel can work in concert with the structured storage and more powerful querying of Access. The results that these two applications can produce together are virtually impossible to achieve with one program separately.
But the book isn't just limited to Excel and Access. There's also a chapter on SQL Server, as well as one dedicated to integrating with other Microsoft Office applications. In no time, you'll discover how to:Utilize the built in features of Access and Excel to access dataUse VBA within Access or Excel to access dataBuild connection strings using ADO and DAOAutomate Excel reports including formatting, functions, and page setupWrite complex functions and queries with VBAWrite simple and advanced queries with the Access GUIProduce pivot tables and charts with your data
With Integrating Excel and Access, you can crunch and visualize data like never before. It's the ideal guide for anyone who uses Microsoft Office to handle data.
Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3.
This book covers:Python data model: understand how special methods are the key to the consistent behavior of objectsData structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode ageFunctions as objects: view Python functions as first-class objects, and understand how this affects popular design patternsObject-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritanceControl flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packagesMetaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work
Rather than impose development guidelines as other frameworks do, Flask leaves the business of extensions up to you. If you have Python experience, this book shows you how to take advantage of that creative freedom.Learn Flask’s basic application structure and write an example appWork with must-have components—templates, databases, web forms, and email supportUse packages and modules to structure a large application that scalesImplement user authentication, roles, and profilesBuild a blogging feature by reusing templates, paginating item lists, and working with rich textUse a Flask-based RESTful API to expose app functionality to smartphones, tablets, and other third-party clientsLearn how to run unit tests and enhance application performanceExplore options for deploying your web app to a production server
You’ll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages.Learn simple data types, and basic math and text operationsUse data-wrangling techniques with Python’s built-in data structuresExplore Python code structure, including the use of functionsWrite large programs in Python, with modules and packagesDive into objects, classes, and other object-oriented featuresExamine storage from flat files to relational databases and NoSQLUse Python to build web clients, servers, APIs, and servicesManage system tasks such as programs, processes, and threadsUnderstand the basics of concurrency and network programming
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
This book 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.Get acquainted with hardware features on the Pi’s board Learn enough Linux to move around the operating system Pick up the basics of Python and Scratch—and start programming Draw graphics, play sounds, and handle mouse events with the Pygame framework Use the Pi’s input and output pins to do some hardware hacking Discover how Arduino and the Raspberry Pi complement each other Integrate USB webcams and other peripherals into your projects Create your own Pi-based web server with Python
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn Detail
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a naïve Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a “whom to follow” recommendation system from Twitter data
Inside, you’ll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works.
Topics include:Data Structures and AlgorithmsStrings and TextNumbers, Dates, and TimesIterators and GeneratorsFiles and I/OData Encoding and ProcessingFunctionsClasses and ObjectsMetaprogrammingModules and PackagesNetwork and Web ProgrammingConcurrencyUtility Scripting and System AdministrationTesting, Debugging, and ExceptionsC Extensions
Ideal for enthusiasts, from students in robotics clubs to professional robotics scientists and engineers, each recipe describes a complete solution using ROS open source libraries and tools. You’ll learn how to complete tasks described in the recipes, as well as how to configure and recombine components for other tasks. If you’re familiar with Python, you’re ready to go.Learn fundamentals, including key ROS concepts, tools, and patternsProgram robots that perform an increasingly complex set of behaviors, using the powerful packages in ROSSee how to easily add perception and navigation abilities to your robotsIntegrate your own sensors, actuators, software libraries, and even a whole robot into the ROS ecosystemLearn tips and tricks for using ROS tools and community resources, debugging robot behavior, and using C++ in ROS
Written by Mark Lutz—widely recognized as the world’s leading Python trainer—Python Pocket Reference is an ideal companion to O’Reilly’s classic Python tutorials, Learning Python and Programming Python, also written by Mark.
This fifth edition covers:Built-in object types, including numbers, lists, dictionaries, and moreStatements and syntax for creating and processing objectsFunctions and modules for structuring and reusing codePython’s object-oriented programming toolsBuilt-in functions, exceptions, and attributesSpecial operator overloading methodsWidely used standard library modules and extensionsCommand-line options and development toolsPython idioms and hintsThe Python SQL Database API
Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide.Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib
Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.
While the Photon--and its accompanying cloud platform--is designed as a ready-to-go foundation for product developers and manufacturers, it's great for Maker projects, as you'll see in this book. You'll learn how to get started with the free development tools, deploy your sketches over WiFi, and build electronic projects that take advantage of the Photon's processing power, cloud platform, and input/output pins. What's more, the Photon is backward-compatible with its predecessor, the Spark Core.
Projects and tutorials include:Controlling devices over BluetoothUsing Python and Arduino programming environments on EdisonTracking objects with a webcam and OpenCVResponding to voice commands and talking backUsing and configuring Linux on Edison
By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.
New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.Develop an understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyImport data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data
Python in easy steps begins by explaining how to install the free Python interpreter so you can quickly begin to create your own executable programs by copying the book's examples. It demonstrates all the Python language basics before moving on to provide examples of Object Oriented Programming (OOP) and CGI scripting to handle web form data. The book concludes by demonstrating how you can use your acquired knowledge to create and deploy graphical windowed applications.
Python in easy steps makes no assumption you have previous knowledge of any programming language so it's ideal for the newcomer to computer programming. It has an easy-to-follow style that will appeal to programmers moving from another programming language, and to the student who is studying Python programming at school or college, and to those seeking a career in computing who need a fundamental understanding of computer programming.
Python is the language used to program the Raspberry Pi - covered by Raspberry Pi in easy steps.
App Engine's Python support includes a fast Python 2.7 interpreter, the standard library, and a WSGI-based runtime environment. Choose from many popular web application frameworks, including Django and Flask.Get a hands-on introduction to App Engine's tools and features, using an example applicationSimulate App Engine on your development machine with tools from Google Cloud SDKStructure your app into individually addressable modules, each with its own scaling configurationExploit the power of the scalable Cloud Datastore, using queries, transactions, and data modeling with the ndb libraryUse Cloud SQL for standard relational databases with App Engine applicationsLearn how to deploy, manage, and inspect your application on Google infrastructure
Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.
Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.
The example code for this unique data science book is maintained in a public GitHub repository. It’s designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks.
Through clear and concise instructions, you’ll learn how to get the most out of App Engine’s nearly unlimited computing power. This second edition is fully updated and expanded to cover Python 2.7 and Java 6 support, multithreading, asynchronous service APIs, and the use of frameworks such as Django 1.3 and webapp2.Understand how App Engine handles web requests and executes application codeLearn about new datastore features for queries and indexes, transactions, and data modelingCreate, manipulate, and serve large data files with the BlobstoreUse task queues to parallelize and distribute computation across the infrastructureEmploy scalable services for email, instant messaging, and communicating with web servicesTrack resource consumption, and optimize your application for speed and cost effectiveness
Bioinformatics Programming Using Python is perfect for anyone involved with bioinformatics -- researchers, support staff, students, and software developers interested in writing bioinformatics applications. You'll find it useful whether you already use Python, write code in another language, or have no programming experience at all. It's an excellent self-instruction tool, as well as a handy reference when facing the challenges of real-life programming tasks.Become familiar with Python's fundamentals, including ways to develop simple applicationsLearn how to use Python modules for pattern matching, structured text processing, online data retrieval, and database accessDiscover generalized patterns that cover a large proportion of how Python code is used in bioinformaticsLearn how to apply the principles and techniques of object-oriented programmingBenefit from the "tips and traps" section in each chapter
Python Projects is the ultimate resource for the Python programmer with basic skills who is ready to move beyond tutorials and start building projects.
The preeminent guide to bridge the gap between learning and doing, this book walks readers through the "where" and "how" of real-world Python programming with practical, actionable instruction. With a focus on real-world functionality, Python Projects details the ways that Python can be used to complete daily tasks and bring efficiency to businesses and individuals alike.
Python Projects is written specifically for those who know the Python syntax and lay of the land, but may still be intimidated by larger, more complex projects. The book provides a walk-through of the basic set-up for an application and the building and packaging for a library, and explains in detail the functionalities related to the projects. Topics include:
*How to maximize the power of the standard library modules
*Where to get third party libraries, and the best practices for utilization
*Creating, packaging, and reusing libraries within and across projects
*Building multi-layered functionality including networks, data, and user interfaces
*Setting up development environments and using virtualenv, pip, and more
Written by veteran Python trainers, the book is structured for easy navigation and logical progression that makes it ideal for individual, classroom, or corporate training.
For Python developers looking to apply their skills to real-world challenges, Python Projects is a goldmine of information and expert insight.
The Arduino is a simple microcontroller with an easy-to-learn programming environment, while the Raspberry Pi is a tiny Linux-based computer. This book clearly explains the differences between the Arduino and Raspberry Pi, when to use them, and to which purposes each are best suited.
Using these widely available and inexpensive platforms, you'll learn to control LEDs, motors of various types, solenoids, AC (alternating current) devices, heaters, coolers, displays, and sound. You'll even discover how to monitor and control these devices over the Internet. Working with solderless breadboards, you'll get up and running quickly, learning how to make projects that are as fun as they are informative. In Make: Action, you'll learn to:Build a can crusher using a linear actuator with your ArduinoHave an Arduino water your plantsBuild a personal traffic signal using LEDsMake a random balloon popper with ArduinoCool down your beverages with a thermostatic drink cooler you build yourselfUnderstand and use the PID control algorithmUse Raspberry Pi to create a puppet dance party that moves to your tweets!
This book is an in-depth guide to the use of pandas for data analysis, for either the seasoned data analysis practitioner or the novice user. It provides a basic introduction to the pandas framework, and takes users through the installation of the library and the IPython interactive environment. Thereafter, you will learn basic as well as advanced features, such as MultiIndexing, modifying data structures, and sampling data, which provide powerful capabilities for data analysis.
This book introduces the Kivy language and the necessary components so you can implement a graphical user interface (GUI) and learn techniques to handle events, detect gestures, and control multi-touch actions. You will learn strategies to animate your applications, and obtain interactive, professional-looking, and responsive results. You will be applying this knowledge throughout the book by developing three applications and tackling their diverse programming challenges.
The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself.
You won't find jargon, bloated samples, case studies, or history of Hello World and computer theory in this handy reference. This Python syntax reference is packed with useful information and is a must-have for any Python developer.
But if you're serious about your profession, intuition isn't enough. Perl Best Practices author Damian Conway explains that rules, conventions, standards, and practices not only help programmers communicate and coordinate with one another, they also provide a reliable framework for thinking about problems, and a common language for expressing solutions. This is especially critical in Perl, because the language is designed to offer many ways to accomplish the same task, and consequently it supports many incompatible dialects.
With a good dose of Aussie humor, Dr. Conway (familiar to many in the Perl community) offers 256 guidelines on the art of coding to help you write better Perl code--in fact, the best Perl code you possibly can. The guidelines cover code layout, naming conventions, choice of data and control structures, program decomposition, interface design and implementation, modularity, object orientation, error handling, testing, and debugging.
They're designed to work together to produce code that is clear, robust, efficient, maintainable, and concise, but Dr. Conway doesn't pretend that this is the one true universal and unequivocal set of best practices. Instead, Perl Best Practices offers coherent and widely applicable suggestions based on real-world experience of how code is actually written, rather than on someone's ivory-tower theories on howsoftware ought to be created.
Most of all, Perl Best Practices offers guidelines that actually work, and that many developers around the world are already using. Much like Perl itself, these guidelines are about helping you to get your job done, without getting in the way.
Praise for Perl Best Practices from Perl community members:
"As a manager of a large Perl project, I'd ensure that every member of my team has a copy of Perl Best Practices on their desk, and use it as the basis for an in-house style guide."-- Randal Schwartz
"There are no more excuses for writing bad Perl programs. All levels of Perl programmer will be more productive after reading this book."-- Peter Scott
"Perl Best Practices will be the next big important book in the evolution of Perl. The ideas and practices Damian lays down will help bring Perl out from under the embarrassing heading of "scripting languages". Many of us have known Perl is a real programming language, worthy of all the tasks normally delegated to Java and C++. With Perl Best Practices, Damian shows specifically how and why, so everyone else can see, too."-- Andy Lester
"Damian's done what many thought impossible: show how to build large, maintainable Perl applications, while still letting Perl be the powerful, expressive language that programmers have loved for years."-- Bill Odom
"Finally, a means to bring lasting order to the process and product of real Perl development teams."-- Andrew Sundstrom"Perl Best Practices provides a valuable education in how to write robust, maintainable Perl, and is a definitive citation source when coaching other programmers."-- Bennett Todd"I've been teaching Perl for years, and find the same question keeps being asked: Where can I find a reference for writing reusable, maintainable Perl code? Finally I have a decent answer."-- Paul Fenwick"At last a well researched, well thought-out, comprehensive guide to Perl style. Instead of each of us developing our own, we can learn good practices from one of Perl's most prolific and experienced authors. I recommend this book to anyone who prefers getting on with the job rather than going back and fixing errors caused by syntax and poor style issues."-- Jacinta Richardson"If you care about programming in any language read this book. Even if you don't intend to follow all of the practices, thinking through your style will improve it."-- Steven Lembark"The Perl community's best author is back with another outstanding book. There has never been a comprehensive reference on high quality Perl coding and style until Perl Best Practices. This book fills a large gap in every Perl bookshelf."-- Uri Guttman
Each chapter includes exercises, using examples that run on Python 3 and Python 2.7. Learn how Kivy simplifies mobile development with its cross-platform API and domain-specific Kv language, and why this free and open source toolkit is ideal for commercial products.Design custom widgets with the Kv languageDelve into Kivy events, event handlers, and propertiesDynamically change which Kivy widgets are displayedUnderstand and apply iterative development principlesCreate basic animations, using Canvas and graphics primitivesStore local data with Kivy’s powerful key value storeAdd basic gestures to switch between app viewsImprove your app’s usability with Kivy’s built-in widgetsDeploy the app to your Android or iOS device, using Buildozer