Search results for "Python in Depth"
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What Every Engineer Should Know About Python
Engineers across all disciplines can benefit from learning Python. This powerful programming language enables engineers to enhance their skill sets and perform more sophisticated work in less time, whether in engineering analysis, system design and development, integration and testing, machine learning and other artificial intelligence applications, project management, or other areas. What Every Engineer Should Know About Python offers students and practicing engineers a straightforward and practical introduction to Python for technical programming and broader uses to enhance productivity. It focuses on the core features of Python most relevant to engineering tasks, avoids computer science jargon, and emphasizes writing useful software while effectively leveraging generative AI.Features examples tied to real-world engineering scenarios that are easily adaptedExplains how to leverage the vast ecosystem of open-source Python packages for scientific applications, rather than developing new software from scratchCovers the incorporation of Python into engineering designs and systems, whether web-based, desktop, or embeddedProvides guidance on optimizing generative AI with Python, including case study examplesDescribes software tool environments and development practices for the rapid creation of high-quality softwareDemonstrates how Python can improve personal and organizational productivity through workflow automationDirects readers to further resources for exploring advanced Python featuresThis practical and concise book serves as a self-contained introduction for engineers and readers from scientific disciplines who are new to programming or to Python.
R 8,262.27
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Python GUI Programming - A Complete Reference Guide
Explore Python’s GUI frameworks and create visually stunning and feature-rich applicationsKey FeaturesIntegrate stunning data visualizations using Tkinter Canvas and MatplotlibUnderstand the basics of 2D and 3D animation in GUI applicationsExplore PyQt’s powerful features to easily design and customize your GUI applicationsBook DescriptionA responsive graphical user interface (GUI) helps you interact with your application, improves user experience, and enhances the efficiency of your applications. With Python, you’ll have access to elaborate GUI frameworks that you can use to build interactive GUIs that stand apart from the rest.This Learning Path begins by introducing you to Tkinter and PyQt, before guiding you through the application development process. As you expand your GUI by adding more widgets, you'll work with networks, databases, and graphical libraries that enhance its functionality. You'll also learn how to connect to external databases and network resources, test your code, and maximize performance using asynchronous programming. In later chapters, you'll understand how to use the cross-platform features of Tkinter and Qt5 to maintain compatibility across platforms. You’ll be able to mimic the platform-native look and feel, and build executables for deployment across popular computing platforms.By the end of this Learning Path, you'll have the skills and confidence to design and build high-end GUI applications that can solve real-world problems.This Learning Path includes content from the following Packt products:Python GUI Programming with Tkinter by Alan D. MooreQt5 Python GUI Programming Cookbook by B. M. HarwaniWhat you will learnVisualize graphs in real time with Tkinter’s animation capabilitiesUse PostgreSQL authentication to ensure data security for your applicationWrite unit tests to avoid regression when updating codeHandle different signals generated on mouse clicks using QSpinBox and slidersEmploy network concepts, internet browsing, and Google Maps in UIUse graphics rendering to implement animations in your GUIWho this book is forIf you’re an intermediate Python programmer looking to enhance your coding skills by writing powerful GUIs in Python using PyQT and Tkinter, this is an ideal Learning Path for you. A strong understanding of the Python language is a must to grasp the concepts explained in this book.
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Impractical Python Projects
Impractical Python picks up where the complete beginners book leaves off, expanding on existing concepts and introducing new tools and techniques that you'll use every day. Just flip to any page, cookbook-style, and test your skills with software design, code optimization, and debugging. To keep thing interesting, each project includes a zany twist featuring historical incidents, pop culture, literature, comics, and the purely scientific.
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Learning Python Networking
Achieve improved network programmability and automation by leveraging powerful network programming concepts, algorithms, and toolsKey FeaturesDeal with remote network servers using SSH, FTP, SNMP and LDAP protocols.Design multi threaded and event-driven architectures for asynchronous servers programming.Leverage your Python programming skills to build powerful network applicationsBook DescriptionNetwork programming has always been a demanding task. With full-featured and well-documented libraries all the way up the stack, Python makes network programming the enjoyable experience it should be.Starting with a walk through of today's major networking protocols, through this book, you'll learn how to employ Python for network programming, how to request and retrieve web resources, and how to extract data in major formats over the web. You will utilize Python for emailing using different protocols, and you'll interact with remote systems and IP and DNS networking. You will cover the connection of networking devices and configuration using Python 3.7, along with cloud-based network management tasks using Python.As the book progresses, socket programming will be covered, followed by how to design servers, and the pros and cons of multithreaded and event-driven architectures. You'll develop practical clientside applications, including web API clients, email clients, SSH, and FTP. These applications will also be implemented through existing web application frameworks.What you will learnExecute Python modules on networking toolsAutomate tasks regarding the analysis and extraction of information from a networkGet to grips with asynchronous programming modules available in PythonGet to grips with IP address manipulation modules using Python programmingUnderstand the main frameworks available in Python that are focused on web applicationManipulate IP addresses and perform CIDR calculationsWho this book is forIf you're a Python developer or a system administrator with Python experience and you're looking to take your first steps in network programming, then this book is for you. If you're a network engineer or a network professional aiming to be more productive and efficient in networking programmability and automation then this book would serve as a useful resource. Basic knowledge of Python is assumed.
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Python Data Structures Pocket Primer
As part of the best-selling Pocket Primer series, this book is designed to present the fundamentals of data structures using Python. Data structures provide a means to managing huge amounts of information such as large databases and the ability to use search and sort algorithms effectively. It is intended to be a fast-paced introduction to the core concepts of Python and data structures, illustrated with numerous code samples. Companion files with source code are available for downloading.
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Python for Scientists
The third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.
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Building Machine Learning Systems with Python -
This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.
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What Every Engineer Should Know About Python
Engineers across all disciplines can benefit from learning Python. This powerful programming language enables engineers to enhance their skill sets and perform more sophisticated work in less time, whether in engineering analysis, system design and development, integration and testing, machine learning and other artificial intelligence applications, project management, or other areas. What Every Engineer Should Know About Python offers students and practicing engineers a straightforward and practical introduction to Python for technical programming and broader uses to enhance productivity. It focuses on the core features of Python most relevant to engineering tasks, avoids computer science jargon, and emphasizes writing useful software while effectively leveraging generative AI.Features examples tied to real-world engineering scenarios that are easily adaptedExplains how to leverage the vast ecosystem of open-source Python packages for scientific applications, rather than developing new software from scratchCovers the incorporation of Python into engineering designs and systems, whether web-based, desktop, or embeddedProvides guidance on optimizing generative AI with Python, including case study examplesDescribes software tool environments and development practices for the rapid creation of high-quality softwareDemonstrates how Python can improve personal and organizational productivity through workflow automationDirects readers to further resources for exploring advanced Python featuresThis practical and concise book serves as a self-contained introduction for engineers and readers from scientific disciplines who are new to programming or to Python.
R 2,711.00
Complex Network Analysis in Python
Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.
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Simulating Nonlinear Circuits with Python Power Electronics
This book provides readers with an in-depth discussion of circuit simulation, combining basic electrical engineering circuit theory with Python programming. It fills an information gap by describing the development of Python Power Electronics, an open-source software for simulating circuits, and demonstrating its use in a sample circuit. Unlike typical books on circuit theory that describe how circuits can be solved mathematically, followed by examples of simulating circuits using specific, commercial software, this book has a different approach and focus. The author begins by describing every aspect of the open-source software, in the context of non-linear power electronic circuits, as a foundation for aspiring or practicing engineers to embark on further development of open source software for different purposes. By demonstrating explicitly the operation of the software through algorithms, this book brings together the fields of electrical engineering and software technology.
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Pro Python Best Practices
Learn software engineering and coding best practices to write Python code right and error free. In this book you’ll see how to properly debug, organize, test, and maintain your code, all of which leads to better, more efficient coding.Software engineering is difficult. Programs of any substantial length are inherently prone to errors of all kinds. The development cycle is full of traps unknown to the apprentice developer. Yet, in Python textbooks little attention is paid to this aspect of getting your code to run. At most, there is a chapter on debugging or unit testing in your average basic Python book. However, the proportion of time spent on getting your code to run is much higher in the real world. Pro Python Best Practices aims to solve this problem.What You'll LearnLearn common debugging techniques that help you find and eliminate errorsGain techniques to detect bugs more easilyWho This Book Is ForExperienced Python coders from web development, big data, and more.
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Finding Ghosts in Your Data
Discover key information buried in the noise of data by learning a variety of anomaly detection techniques and using the Python programming language to build a robust service for anomaly detection against a variety of data types. The book starts with an overview of what anomalies and outliers are and uses the Gestalt school of psychology to explain just why it is that humans are naturally great at detecting anomalies. From there, you will move into technical definitions of anomalies, moving beyond "I know it when I see it" to defining things in a way that computers can understand.The core of the book involves building a robust, deployable anomaly detection service in Python. You will start with a simple anomaly detection service, which will expand over the course of the book to include a variety of valuable anomaly detection techniques, covering descriptive statistics, clustering, and time series scenarios. Finally, you will compare your anomaly detection service head-to-head with a publicly available cloud offering and see how they perform.The anomaly detection techniques and examples in this book combine psychology, statistics, mathematics, and Python programming in a way that is easily accessible to software developers. They give you an understanding of what anomalies are and why you are naturally a gifted anomaly detector. Then, they help you to translate your human techniques into algorithms that can be used to program computers to automate the process. You’ll develop your own anomaly detection service, extend it using a variety of techniques such as including clustering techniques for multivariate analysis and time series techniques for observing data over time, and compare your service head-on against a commercial service.What You Will LearnUnderstand the intuition behind anomaliesConvert your intuition into technical descriptions of anomalous dataDetect anomalies using statistical tools, such as distributions, variance and standard deviation, robust statistics, and interquartile rangeApply state-of-the-art anomaly detection techniques in the realms of clustering and time series analysisWork with common Python packages for outlier detection and time series analysis, such as scikit-learn, PyOD, and tslearnDevelop a project from the ground up which finds anomalies in data, starting with simple arrays of numeric data and expanding to include multivariate inputs and even time series dataWho This Book Is ForFor software developers with at least some familiarity with the Python programming language, and who would like to understand the science and some of the statistics behind anomaly detection techniques. Readers are not required to have any formal knowledge of statistics as the book introduces relevant concepts along the way.
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Introduction to Python for Science and Engineering
Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed.Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms.Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments.All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.
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