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Mathematica Cookbook helps you master the application's core principles by walking you through real-world problems. Ideal for browsing, this book includes recipes for working with numerics, data structures, algebraic equations, calculus, and statistics. You'll also venture into exotic territory with recipes for data visualization using 2D and 3D graphic tools, image processing, and music. Although Mathematica 7 is a highly advanced computational platform, the recipes in this book make it accessible to everyone -- whether you're working on high school algebra, simple graphs, PhD-level computation, financial analysis, or advanced engineering models. Learn how to use Mathematica at a higher level with functional programming and pattern matching Delve into the rich library of functions for string and structured text manipulation Learn how to apply the tools to physics and engineering problems Draw on Mathematica's access to physics, chemistry, and biology data Get techniques for solving equations in computational finance Learn how to use Mathematica for sophisticated image processing Process music and audio as musical notes, analog waveforms, or digital sound samples
In the dynamic realm of Python programming, dictionaries stand out as one of the most versatile and efficient data structures available. This book delves deep into the full potential of Python dictionaries, exploring their fundamental operations, practical applications, and their pivotal role in data science, software development, and graphical user interface (GUI) design using Tkinter. Dictionaries in Python are analogous to real-world dictionaries; they consist of key-value pairs that provide a fast and straightforward way to store and manage data. Unlike lists or arrays where elements are accessed via their position, dictionaries allow for quicker access through unique keys, making them indispensable for handling large datasets where speed and efficiency are crucial. The early chapters of this book introduce the basic operations associated with dictionaries, such as adding, removing, and modifying items. Each concept is reinforced with clear, practical examples demonstrating how these operations are used in everyday coding tasks. We also delve into more complex dictionary methods that enhance functionality, such as get(), keys(), values(), and items() methods, which facilitate efficient data retrieval and manipulation. As we progress, the book explores advanced applications of dictionaries in Python, including their use in web development for managing data, configuring settings in applications, and handling feature management in machine learning algorithms. The versatility of dictionaries is also showcased in tasks like JSON data parsing and management, where dictionaries’ ability to nest and store complex data structures is particularly beneficial. One of the highlights of this book is the integration of dictionaries with Python’s powerful libraries for data analysis and visualization, such as Pandas and Matplotlib. This includes examples of converting dictionaries into Pandas DataFrames to simplify data analysis tasks, or using dictionaries to store data points for graphical representation. Moreover, we introduce the development of graphical user interfaces using Tkinter, where dictionaries play a critical role in managing the state and properties of GUI elements. You will learn how to dynamically update GUI components based on user interactions stored and manipulated through dictionaries. This not only enhances the functionality of your applications but also showcases the synergy between data management and interface design. In addition to practical applications, the book addresses the performance aspects of dictionaries, comparing their efficiency with other data structures in Python. This discussion extends into real-world scenarios, demonstrating how dictionaries can be optimized for performance and memory usage in large-scale applications. We also examine common pitfalls and best practices when working with dictionaries to help you avoid common errors and improve the readability and efficiency of your code. From simple tasks like populating a dictionary with data, to more complex scenarios involving dictionaries within dictionaries, this book provides you with the knowledge to use dictionaries effectively in your programming projects. Lastly, this book doesn’t just teach you how to use dictionaries; it inspires you to think more deeply about data structuring and management. By integrating dictionary operations with Tkinter GUI development, you will gain a comprehensive understanding of how these tools can be combined to create more interactive and user-friendly applications. Whether you are a data scientist, a backend developer, or a software engineer, mastering dictionaries and Tkinter will enhance your coding toolkit and open up new horizons in your software development career.
Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more. Get a better grasp of NumPy, Cython, and profilers Learn how Python abstracts the underlying computer architecture Use profiling to find bottlenecks in CPU time and memory usage Write efficient programs by choosing appropriate data structures Speed up matrix and vector computations Use tools to compile Python down to machine code Manage multiple I/O and computational operations concurrently Convert multiprocessing code to run on local or remote clusters Deploy code faster using tools like Docker
If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms. Inside, youâ??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 Algorithms Strings and Text Numbers, Dates, and Times Iterators and Generators Files and I/O Data Encoding and Processing Functions Classes and Objects Metaprogramming Modules and Packages Network and Web Programming Concurrency Utility Scripting and System Administration Testing, Debugging, and Exceptions C Extensions
The Information System Consultant's Handbook familiarizes systems analysts, systems designers, and information systems consultants with underlying principles, specific documentation, and methodologies. Corresponding to the primary stages in the systems development life cycle, the book divides into eight sections: Principles Information Gathering and Problem Definition Project Planning and Project Management Systems Analysis Identifying Alternatives Component Design Testing and Implementation Operation and Maintenance Eighty-two chapters comprise the book, and each chapter covers a single tool, technique, set of principles, or methodology. The clear, concise narrative, supplemented with numerous illustrations and diagrams, makes the material accessible for readers - effectively outlining new and unfamiliar analysis and design topics.
The second edition of this bestselling title is a perfect blend of theoretical knowledge and practical application. It progresses gradually from basic to advance concepts in database management systems, with numerous solved exercises to make learning easier and interesting. New to this edition are discussions on more commercial database management systems.
C++ was written to help professional C# developers learn modern C++ programming. The aim of this book is to leverage your existing C# knowledge in order to expand your skills. Whether you need to use C++ in an upcoming project, or simply want to learn a new language (or reacquaint yourself with it), this book will help you learn all of the fundamental pieces of C++ so you can begin writing your own C++ programs.This updated and expanded second edition of Book provides a user-friendly introduction to the subject, Taking a clear structural framework, it guides the reader through the subject's core elements. A flowing writing style combines with the use of illustrations and diagrams throughout the text to ensure the reader understands even the most complex of concepts. This succinct and enlightening overview is a required reading for all those interested in the subject .We hope you find this book useful in shaping your future career & Business.
A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts: Introduction to Unix/Linux: The command-line is the "natural environment" of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful "pipe" operator for file and data manipulation. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2.
Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level that allows students to quickly apply results in practical settings. - Includes tips, warnings and "try this" features within each chapter to help the reader develop good programming practice - Summaries at the end of each chapter allow for quick access to important information - Includes code in Jupyter notebook format that can be directly run online
The Handbook of Data Structures and Applications was first published over a decade ago. This second edition aims to update the first by focusing on areas of research in data structures that have seen significant progress. While the discipline of data structures has not matured as rapidly as other areas of computer science, the book aims to update those areas that have seen advances. Retaining the seven-part structure of the first edition, the handbook begins with a review of introductory material, followed by a discussion of well-known classes of data structures, Priority Queues, Dictionary Structures, and Multidimensional structures. The editors next analyze miscellaneous data structures, which are well-known structures that elude easy classification. The book then addresses mechanisms and tools that were developed to facilitate the use of data structures in real programs. It concludes with an examination of the applications of data structures. Four new chapters have been added on Bloom Filters, Binary Decision Diagrams, Data Structures for Cheminformatics, and Data Structures for Big Data Stores, and updates have been made to other chapters that appeared in the first edition. The Handbook is invaluable for suggesting new ideas for research in data structures, and for revealing application contexts in which they can be deployed. Practitioners devising algorithms will gain insight into organizing data, allowing them to solve algorithmic problems more efficiently.