Python And Xml

This book has two objectives--to provide a comprehensive reference on using XML with Python; and to illustrate the practical applications of these technologies in an enterprise environment with examples.

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* Quick start to learning python—very example oriented approach * Book has its own Web site established by the author: http://diveintopython.org/ Author is well known in the Open Source community and the book has a unique quick approach to learning an object oriented language.

This second edition of the bestselling Learning XML provides web developers with a concise but grounded understanding of XML (the Extensible Markup Language) and its potential-- not just a whirlwind tour of XML.The author explains the important and relevant XML technologies and their capabilities clearly and succinctly with plenty of real-life projects and useful examples. He outlines the elements of markup--demystifying concepts such as attributes, entities, and namespaces--and provides enough depth and examples to get started. Learning XML is a reliable source for anyone who needs to know XML, but doesn't want to waste time wading through hundreds of web sites or 800 pages of bloated text.For writers producing XML documents, this book clarifies files and the process of creating them with the appropriate structure and format. Designers will learn what parts of XML are most helpful to their team and will get started on creating Document Type Definitions. For programmers, the book makes syntax and structures clear. Learning XML also discusses the stylesheets needed for viewing documents in the next generation of browsers, databases, and other devices.Learning XML illustrates the core XML concepts and language syntax, in addition to important related tools such as the CSS and XSL styling languages and the XLink and XPointer specifications for creating rich link structures. It includes information about three schema languages for validation: W3C Schema, Schematron, and RELAX-NG, which are gaining widespread support from people who need to validate documents but aren't satisfied with DTDs. Also new in this edition is a chapter on XSL-FO, a powerful formatting language for XML. If you need to wade through the acronym soup of XML and start to really use this powerful tool, Learning XML, will give you the roadmap you need.

Portable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for both standalone programs and scripting applications. It is now being used by an increasing number of major organizations, including NASA and Google.Updated for Python 2.4, The Python Cookbook, 2nd Edition offers a wealth of useful code for all Python programmers, not just advanced practitioners. Like its predecessor, the new edition provides solutions to problems that Python programmers face everyday.It now includes over 200 recipes that range from simple tasks, such as working with dictionaries and list comprehensions, to complex tasks, such as monitoring a network and building a templating system. This revised version also includes new chapters on topics such as time, money, and metaprogramming.Here's a list of additional topics covered: Manipulating text Searching and sorting Working with files and the filesystem Object-oriented programming Dealing with threads and processes System administration Interacting with databases Creating user interfaces Network and web programming Processing XML Distributed programming Debugging and testing Another advantage of The Python Cookbook, 2nd Edition is its trio of authors--three well-known Python programming experts, who are highly visible on email lists and in newsgroups, and speak often at Python conferences.With scores of practical examples and pertinent background information, The Python Cookbook, 2nd Edition is the one source you need if you're looking to build efficient, flexible, scalable, and well-integrated systems.

Over 60 practical recipes on data exploration and analysis About This Book Clean dirty data, extract accurate information, and explore the relationships between variables Forecast the output of an electric plant and the water flow of American rivers using pandas, NumPy, Statsmodels, and scikit-learn Find and extract the most important features from your dataset using the most efficient Python libraries Who This Book Is For If you are a beginner or intermediate-level professional who is looking to solve your day-to-day, analytical problems with Python, this book is for you. Even with no prior programming and data analytics experience, you will be able to finish each recipe and learn while doing so. What You Will Learn Read, clean, transform, and store your data usng Pandas and OpenRefine Understand your data and explore the relationships between variables using Pandas and D3.js Explore a variety of techniques to classify and cluster outbound marketing campaign calls data of a bank using Pandas, mlpy, NumPy, and Statsmodels Reduce the dimensionality of your dataset and extract the most important features with pandas, NumPy, and mlpy Predict the output of a power plant with regression models and forecast water flow of American rivers with time series methods using pandas, NumPy, Statsmodels, and scikit-learn Explore social interactions and identify fraudulent activities with graph theory concepts using NetworkX and Gephi Scrape Internet web pages using urlib and BeautifulSoup and get to know natural language processing techniques to classify movies ratings using NLTK Study simulation techniques in an example of a gas station with agent-based modeling In Detail Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors. This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more. First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews. By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer. Style and approach This hands-on recipe guide is divided into three sections that tackle and overcome real-world data modeling problems faced by data analysts/scientist in their everyday work. Each independent recipe is written in an easy-to-follow and step-by-step fashion.

Have you ever needed to share processing between two or more computers running programs written in different languages on different operating systems? Or have you ever wanted to publish information on the Web so that programs other than browsers could work with it? XML-RPC, a system for remote procedure calls built on XML and the ubiquitous HTTP protocol, is the solution you've been looking for.Programming Web Services with XML-RPC introduces the simple but powerful capabilities of XML-RPC, which lets you connect programs running on different computers with a minimum of fuss, by wrapping procedure calls in XML and establishing simple pathways for calling functions. With XML-RPC, Java programs can talk to Perl scripts, which can talk to Python programs, ASP applications, and so on. You can provide access to procedure calls without having to worry about the system on the other end, so it's easy to create services that are available on the Web.XML-RPC isn't the only solution for web services; the Simple Object Access Protocol (SOAP) is another much-hyped protocol for implementing web services. While XML-RPC provides fewer capabilities than SOAP, it also has far fewer interoperability problems and its capabilities and limitations are much better understood. XML-RPC is also stable, with over 30 implementations on a wide variety of platforms, so you can start doing real work with it immediately.Programming Web Services with XML-RPC covers the details of five XML-RPC implementations, so you can get started developing distributed applications in Java, Perl, Python, ASP, or PHP. The chapters on these implementations contain code examples that you can use as the basis for your own work. This book also provides in-depth coverage of the XML-RPC specification, which is helpful for low-level debugging of XML-RPC clients and servers. And if you want to build your own XML-RPC implementation for another environment, the detailed explanations in this book will serve as a foundation for that work.

Build optimized applications in Python by smartly implementing the standard library Key Features Strategic recipes for effective application development in Python Techniques to create GUIs and implement security through cryptography Best practices for developing readily scalable, production-ready applications Book Description The Python 3 Standard Library is a vast array of modules that you can use for developing various kinds of applications. It contains an exhaustive list of libraries, and this book will help you choose the best one to address specific programming problems in Python. The Modern Python Standard Library Cookbook begins with recipes on containers and data structures and guides you in performing effective text management in Python. You will find Python recipes for command-line operations, networking, filesystems and directories, and concurrent execution. You will learn about Python security essentials in Python and get to grips with various development tools for debugging, benchmarking, inspection, error reporting, and tracing. The book includes recipes to help you create graphical user interfaces for your application. You will learn to work with multimedia components and perform mathematical operations on date and time. The recipes will also show you how to deploy different searching and sorting algorithms on your data. By the end of the book, you will have acquired the skills needed to write clean code in Python and develop applications that meet your needs. What you will learn Store multiple values per key in associative containers Create interactive character-based user interfaces Work with native time and display data for your time zone Read/write SGML family languages, both as a SAX and DOM parser to meet file sizes and other requirements Group equivalent items using itertools and sorted features together Use partials to create unary functions out of multi-argument functions Implement hashing algorithms to store passwords in a safe way Who this book is for If you are a developer who wants to write highly responsive, manageable, scalable, and resilient code in Python, this book is for you. Prior programming knowledge in Python will help you make the most out of the book.